1
|
Kaźmierczak I, Jakubowska A, Pietraszkiewicz A, Zajenkowska A, Lacko D, Wawer A, Sarzyńska-Wawer J. Natural language sentiment as an indicator of depression and anxiety symptoms: a longitudinal mixed methods study 1. Cogn Emot 2024:1-10. [PMID: 38738660 DOI: 10.1080/02699931.2024.2351952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/30/2024] [Indexed: 05/14/2024]
Abstract
The study tested how the use of positive- (e.g. beautiful) and negative-valenced (e.g. horrible) words in natural language and its change in time affects the severity of depression and anxiety symptoms among depressed and non-depressed individuals. This longitudinal mixed methods study (N = 40 participants, n = 1440 narratives) with three measurements within a year showed that at the between-person level the use of negative-valenced words was strongly associated with the increase in anxiety and depression symptoms over time while the use of positive-valenced words was slightly associated with the decrease in anxiety and depression symptom. These effects were not supported for within-person level (i.e. changes in word usage). No significant differences were observed in the effects between depressed and non-depressed groups. Summing up, the overall use of positive- and negative-valenced words (particularly negative-valenced words) had a stronger effect on the severity of psychopathological symptoms than their change over time. The results were discussed in the context of natural language processing and its application in diagnosing depression and anxiety symptoms.
Collapse
Affiliation(s)
| | | | | | | | - David Lacko
- Institute of Psychology, Czech Academy of Sciences, Brno, Czechia
| | - Aleksander Wawer
- Institute of Computer Science, Polish Academy of Science, Warsaw, Poland
| | | |
Collapse
|
2
|
Rai S, Stade EC, Giorgi S, Francisco A, Ungar LH, Curtis B, Guntuku SC. Key language markers of depression on social media depend on race. Proc Natl Acad Sci U S A 2024; 121:e2319837121. [PMID: 38530887 PMCID: PMC10998627 DOI: 10.1073/pnas.2319837121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/31/2024] [Indexed: 03/28/2024] Open
Abstract
Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice.
Collapse
Affiliation(s)
- Sunny Rai
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA19104
| | - Elizabeth C. Stade
- Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA94305
| | - Salvatore Giorgi
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA19104
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD21224
| | - Ashley Francisco
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA19104
| | - Lyle H. Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA19104
| | - Brenda Curtis
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD21224
| | - Sharath C. Guntuku
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA19104
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA19104
| |
Collapse
|
3
|
Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
Collapse
Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
| |
Collapse
|
4
|
Aldkheel A, Zhou L. Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2024; 8:88-120. [PMID: 38273983 PMCID: PMC10805697 DOI: 10.1007/s41666-023-00152-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 10/24/2023] [Accepted: 11/06/2023] [Indexed: 01/27/2024]
Abstract
Social media has become a safe space for discussing sensitive topics such as mental disorders. Depression dominates mental disorders globally, and accordingly, depression detection on social media has witnessed significant research advances. This study aims to review the current state-of-the-art research methods and propose a multidimensional framework to describe the current body of literature relating to detecting depression on social media. A study methodology involved selecting papers published between 2011 and 2023 that focused on detecting depression on social media. Five digital libraries were used to find relevant papers: Google Scholar, ACM digital library, PubMed, IEEE Xplore and ResearchGate. In selecting literature, two fundamental elements were considered: identifying papers focusing on depression detection and including papers involving social media use. In total, 50 papers were reviewed. Multiple dimensions were analyzed, including input features, social media platforms, disorder and symptomatology, ground truth, and techniques. Various types of input features were employed for depression detection, including textual, visual, behavioral, temporal, demographic, and spatial features. Among them, visual and spatial features have not been systematically reviewed to support mental health researchers in depression detection. Despite depression's fine-grained disorders, most studies focus on general depression. Recent studies have shown that social media data can be leveraged to identify depressive symptoms. Nevertheless, further research is needed to address issues like depression validation, generalizability, causes identification, and privacy and ethical considerations. An interdisciplinary collaboration between mental health professionals and computer scientists may help detect depression on social media more effectively.
Collapse
Affiliation(s)
- Abdulrahman Aldkheel
- Department of Software and Information Systems, The University of North Carolina at Charlotte, Charlotte, NC USA
| | - Lina Zhou
- Department of Business Information Systems and Operations Management, The University of North Carolina at Charlotte, Charlotte, NC USA
| |
Collapse
|
5
|
Clay PA, Asher JM, Carnes N, Copen CE, Delaney KP, Payne DC, Pollock ED, Mermin J, Nakazawa Y, Still W, Mangla AT, Spicknall IH. Modelling the impact of vaccination and sexual behaviour adaptations on mpox cases in the USA during the 2022 outbreak. Sex Transm Infect 2024; 100:70-76. [PMID: 38050171 DOI: 10.1136/sextrans-2023-055922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 10/22/2023] [Indexed: 12/06/2023] Open
Abstract
BACKGROUND The 2022 mpox outbreak has infected over 30 000 people in the USA, with cases declining since mid-August. Infections were commonly associated with sexual contact between men. Interventions to mitigate the outbreak included vaccination and a reduction in sexual partnerships. Understanding the contributions of these interventions to decreasing cases can inform future public health efforts. METHODS We fit a dynamic network transmission model to mpox cases reported by Washington DC through 10 January 2023. This model incorporated both vaccine administration data and reported reductions in sexual partner acquisition by gay, bisexual or other men who have sex with men (MSM). The model output consisted of daily cases over time with or without vaccination and/or behavioural adaptation. RESULTS We found that initial declines in cases were likely caused by behavioural adaptations. One year into the outbreak, vaccination and behavioural adaptation together prevented an estimated 84% (IQR 67% to 91%) of cases. Vaccination alone averted 79% (IQR 64% to 88%) of cases and behavioural adaptation alone averted 25% (IQR 10% to 42%) of cases. We further found that in the absence of vaccination, behavioural adaptation would have reduced the number of cases, but would have prolonged the outbreak. CONCLUSIONS We found that initial declines in cases were likely caused by behavioural adaptation, but vaccination averted more cases overall and was key to hastening outbreak conclusion. Overall, this indicates that outreach to encourage individuals to protect themselves from infection was vital in the early stages of the mpox outbreak, but that combination with a robust vaccination programme hastened outbreak conclusion.
Collapse
Affiliation(s)
- Patrick A Clay
- Division of STD Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jason M Asher
- Office of the Director, Center for Forecasting and Outbreak Analytics, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Neal Carnes
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Casey E Copen
- Division of STD Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Kevin P Delaney
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Daniel C Payne
- Division of Foodborne, Waterborne & Environmental Diseases, National Center for Emerging & Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Emily D Pollock
- Division of STD Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jonathan Mermin
- Office of the Director, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Yoshinori Nakazawa
- Division of HIV Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - William Still
- DC Department of Health, Washington, District of Columbia, USA
| | - Anil T Mangla
- DC Department of Health, Washington, District of Columbia, USA
| | - Ian H Spicknall
- Division of STD Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| |
Collapse
|
6
|
Efe Z, Baldofski S, Kohls E, Eckert M, Saee S, Thomas J, Wundrack R, Rummel-Kluge C. Linguistic Variables and Gender Differences Within a Messenger-Based Psychosocial Chat Counseling Service for Children and Adolescents: Cross-Sectional Study. JMIR Form Res 2024; 8:e51795. [PMID: 38214955 PMCID: PMC10818237 DOI: 10.2196/51795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/29/2023] [Accepted: 11/29/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND Text messaging is widely used by young people for communicating and seeking mental health support through chat-based helplines. However, written communication lacks nonverbal cues, and language usage is an important source of information about a person's mental health state and is known to be a marker for psychopathology. OBJECTIVE The aim of the study was to investigate language usage, and its gender differences and associations with the presence of psychiatric symptoms within a chat counseling service for adolescents and young adults. METHODS For this study, the anonymized chat content of a German messenger-based psychosocial chat counseling service for children and adolescents ("krisenchat") between May 2020 and July 2021 was analyzed. In total, 661,131 messages from 6962 users were evaluated using Linguistic Inquiry and Word Count, considering the following linguistic variables: first-person singular and plural pronouns, negations, positive and negative emotion words, insight words, and causation words. Descriptive analyses were performed, and gender differences of those variables were evaluated. Finally, a binary logistic regression analysis examined the predictive value of linguistic variables on the presence of psychiatric symptoms. RESULTS Across all analyzed chats, first-person singular pronouns were used most frequently (965,542/8,328,309, 11.6%), followed by positive emotion words (408,087/8,328,309, 4.9%), insight words (341,460/8,328,309, 4.1%), negations (316,475/8,328,309, 3.8%), negative emotion words (266,505/8,328,309, 3.2%), causation words (241,520/8,328,309, 2.9%), and first-person plural pronouns (499,698/8,328,309, 0.6%). Female users and users identifying as diverse used significantly more first-person singular pronouns and insight words than male users (both P<.001). Negations were significantly more used by female users than male users or users identifying as diverse (P=.007). Similar findings were noted for negative emotion words (P=.01). The regression model of predicting psychiatric symptoms by linguistic variables was significant and indicated that increased use of first-person singular pronouns (odds ratio [OR] 1.05), negations (OR 1.11), and negative emotion words (OR 1.15) was positively associated with the presence of psychiatric symptoms, whereas increased use of first-person plural pronouns (OR 0.39) and causation words (OR 0.90) was negatively associated with the presence of psychiatric symptoms. Suicidality, self-harm, and depression showed the most significant correlations with linguistic variables. CONCLUSIONS This study highlights the importance of examining linguistic features in chat counseling contexts. By integrating psycholinguistic findings into counseling practice, counselors may better understand users' psychological processes and provide more targeted support. For instance, certain linguistic features, such as high use of first-person singular pronouns, negations, or negative emotion words, may indicate the presence of psychiatric symptoms, particularly among female users and users identifying as diverse. Further research is needed to provide an in-depth look into language processes within chat counseling services.
Collapse
Affiliation(s)
- Zeki Efe
- Department of Psychiatry and Psychotherapy, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Sabrina Baldofski
- Department of Psychiatry and Psychotherapy, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Elisabeth Kohls
- Department of Psychiatry and Psychotherapy, Medical Faculty, Leipzig University, Leipzig, Germany
- Department of Psychiatry and Psychotherapy, University Leipzig Medical Center, Leipzig University, Leipzig, Germany
| | | | | | | | - Richard Wundrack
- Krisenchat gGmbH, Berlin, Germany
- Department of Psychology, Chair of Personality Psychology, Humboldt Universität zu Berlin, Berlin, Germany
| | - Christine Rummel-Kluge
- Department of Psychiatry and Psychotherapy, Medical Faculty, Leipzig University, Leipzig, Germany
- Department of Psychiatry and Psychotherapy, University Leipzig Medical Center, Leipzig University, Leipzig, Germany
| |
Collapse
|
7
|
Yin Y, Hanes DW, Skiena S, Clouston SAP. Quantifying Healthy Aging in Older Veterans Using Computational Audio Analysis. J Gerontol A Biol Sci Med Sci 2024; 79:glad154. [PMID: 37366320 PMCID: PMC10733188 DOI: 10.1093/gerona/glad154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Researchers are increasingly interested in better methods for assessing the pace of aging in older adults, including vocal analysis. The present study sought to determine whether paralinguistic vocal attributes improve estimates of the age and risk of mortality in older adults. METHODS To measure vocal age, we curated interviews provided by male U.S. World War II Veterans in the Library of Congress collection. We used diarization to identify speakers and measure vocal features and matched recording data to mortality information. Veterans (N = 2 447) were randomly split into testing (n = 1 467) and validation (n = 980) subsets to generate estimations of vocal age and years of life remaining. Results were replicated to examine out-of-sample utility using Korean War Veterans (N = 352). RESULTS World War II Veterans' average age was 86.08 at the time of recording and 91.28 at the time of death. Overall, 7.4% were prisoners of war, 43.3% were Army Veterans, and 29.3% were drafted. Vocal age estimates (mean absolute error = 3.255) were within 5 years of chronological age, 78.5% of the time. With chronological age held constant, older vocal age estimation was correlated with shorter life expectancy (aHR = 1.10; 95% confidence interval: 1.06-1.15; p < .001), even when adjusting for age at vocal assessment. CONCLUSIONS Computational analyses reduced estimation error by 71.94% (approximately 8 years) and produced vocal age estimates that were correlated with both age and predicted time until death when age was held constant. Paralinguistic analyses augment other assessments for individuals when oral patient histories are recorded.
Collapse
Affiliation(s)
- Yunting Yin
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Douglas William Hanes
- Program in Public Health, Department of Family, Population, and Preventive Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Steven Skiena
- Department of Computer Science, Stony Brook University, Stony Brook, New York, USA
| | - Sean A P Clouston
- Program in Public Health, Department of Family, Population, and Preventive Medicine, Stony Brook University, Stony Brook, New York, USA
| |
Collapse
|
8
|
Zarate D, Ball M, Prokofieva M, Kostakos V, Stavropoulos V. Identifying self-disclosed anxiety on Twitter: A natural language processing approach. Psychiatry Res 2023; 330:115579. [PMID: 37956589 DOI: 10.1016/j.psychres.2023.115579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 09/13/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023]
Abstract
BACKGROUND Text analyses of social media posts are a promising source of mental health information. This study used natural language processing to explore distinct language patterns on Twitter related to self-reported anxiety diagnosis. METHODS A total of 233.000 tweets made by 605 users (300 reporting anxiety diagnosis and 305 not) over six months were comparatively analysed, considering user behavior, Linguistic Inquiry Word Count (LIWC), and sentiment analysis. Twitter users with a self-disclosed diagnosis of anxiety were classified as 'anxious' to facilitate group comparisons. RESULTS Supervised machine learning models showed a high prediction accuracy (Naïve Bayes 81.1 %, Random Forests 79.8 %, and LASSO-regression 79.4 %) in identifying Twitter users' self-disclosed diagnosis of anxiety. Additionally, a Latent Profile Analysis (LPA) identified four profiles characterized by high sentiment (31 % anxious participants), low sentiment (68 % anxious), self-immersed (80 % anxious), and normative behavior (38 % anxious). CONCLUSION The digital footprint of self-disclosed anxiety on Twitter posts presented a high frequency of words conveying either negative sentiment, a low frequency of positive sentiment, a reduced frequency of posting, and lengthier texts. These distinct patterns enabled highly accurate prediction of anxiety diagnosis. On this basis, appropriately resourced, awareness raising, online mental health campaigns are advocated.
Collapse
Affiliation(s)
- Daniel Zarate
- College of Health and Biomedicine, Royal Melbourne Institute of Technology (RMIT), Australia.
| | - Michelle Ball
- Institute for Health and Sport, Victoria University, Melbourne, Australia
| | - Maria Prokofieva
- Institute for Health and Sport, Victoria University, Melbourne, Australia
| | | | - Vasileios Stavropoulos
- College of Health and Biomedicine, Royal Melbourne Institute of Technology (RMIT), Australia; Department of Psychology, University of Athens, Athens, Greece
| |
Collapse
|
9
|
Garcia Whitlock AE, Gill BP, Richardson JB, Patton DU, Strong B, Nwakanma CC, Kaufman EJ. Analysis of Social Media Involvement in Violent Injury. JAMA Surg 2023; 158:1347-1349. [PMID: 37819673 PMCID: PMC10568437 DOI: 10.1001/jamasurg.2023.4995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/16/2023] [Indexed: 10/13/2023]
Abstract
This cross-sectional study uses police agency–collected information to quantify the association among social media involvement, crime, and violence.
Collapse
Affiliation(s)
- Anna E. Garcia Whitlock
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Brendan P. Gill
- Prince George’s County Police Department, Upper Marlboro, Maryland
| | - Joseph B. Richardson
- Departments of African-American Studies and Medical Anthropology, University of Maryland, Baltimore, Maryland
| | - Desmond U. Patton
- University of Pennsylvania School of Social Policy and Practice, Philadelphia, Pennsylvania
| | - Bethany Strong
- Department of Surgery, University of Maryland, Baltimore, Maryland
| | - Chidinma C. Nwakanma
- Department of Emergency Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Elinore J. Kaufman
- Division of Traumatology, Surgical Critical Care, and Emergency Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| |
Collapse
|
10
|
Mao K, Wu Y, Chen J. A systematic review on automated clinical depression diagnosis. NPJ MENTAL HEALTH RESEARCH 2023; 2:20. [PMID: 38609509 PMCID: PMC10955993 DOI: 10.1038/s44184-023-00040-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/27/2023] [Indexed: 04/14/2024]
Abstract
Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.
Collapse
Affiliation(s)
- Kaining Mao
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Yuqi Wu
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada
| | - Jie Chen
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
| |
Collapse
|
11
|
Sametoğlu S, Pelt DHM, Eichstaedt JC, Ungar LH, Bartels M. Comparison of wellbeing structures based on survey responses and social media language: A network analysis. Appl Psychol Health Well Being 2023; 15:1555-1582. [PMID: 37161901 DOI: 10.1111/aphw.12451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/07/2023] [Indexed: 05/11/2023]
Abstract
Wellbeing is predominantly measured through surveys but is increasingly measured by analysing individuals' language on social media platforms using social media text mining (SMTM). To investigate whether the structure of wellbeing is similar across both data collection methods, we compared networks derived from survey items and social media language features collected from the same participants. The dataset was split into an independent exploration (n = 1169) and a final subset (n = 1000). After estimating exploration networks, redundant survey items and language topics were eliminated. Final networks were then estimated using exploratory graph analysis (EGA). The networks of survey items and those from language topics were similar, both consisting of five wellbeing dimensions. The dimensions in the survey- and SMTM-based assessment of wellbeing showed convergent structures congruent with theories of wellbeing. Specific dimensions found in each network reflected the unique aspects of each type of data (survey and social media language). Networks derived from both language features and survey items show similar structures. Survey and SMTM methods may provide complementary methods to understand differences in human wellbeing.
Collapse
Affiliation(s)
- Selim Sametoğlu
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Dirk H M Pelt
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Johannes C Eichstaedt
- Department of Psychology, Stanford University, Stanford, California, USA
- Institute for Human-Centered AI, Stanford University, Stanford, California, USA
| | - Lyle H Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Positive Psychology Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| |
Collapse
|
12
|
Gopalakrishnan A, Gururajan R, Venkataraman R, Zhou X, Ching KC, Saravanan A, Sen M. Attribute Selection Hybrid Network Model for risk factors analysis of postpartum depression using Social media. Brain Inform 2023; 10:28. [PMID: 37906324 PMCID: PMC10618142 DOI: 10.1186/s40708-023-00206-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 09/16/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Postpartum Depression (PPD) is a frequently ignored birth-related consequence. Social network analysis can be used to address this issue because social media network serves as a platform for their users to communicate with their friends and share their opinions, photos, and videos, which reflect their moods, feelings, and sentiments. In this work, the depression of delivered mothers is identified using the PPD score and segregated into control and depressed groups. Recently, to detect depression, deep learning methods have played a vital role. However, these methods still do not clarify why some people have been identified as depressed. METHODS We have developed Attribute Selection Hybrid Network (ASHN) to detect the postpartum depression diagnoses framework. Later analysis of the post of mothers who have been confirmed with the score calculated by the experts of the field using physiological questionnaire score. The model works on the analysis of the attributes of the negative Facebook posts for Depressed user Diagnosis, which is a large general forum. This framework explains the process of analyzing posts containing Sentiment, depressive symptoms, and reflective thinking and suggests psycho-linguistic and stylistic attributes of depression in posts. RESULTS The experimental results show that ASHN works well and is easy to understand. Here, four attribute networks based on psychological studies were used to analyze the different parts of posts by depressed users. The results of the experiments show the extraction of psycho-linguistic markers-based attributes, the recording of assessment metrics including Precision, Recall and F1 score and visualization of those attributes were used title-wise as well as words wise and compared with daily life, depression and postpartum depressed people using Word cloud. Furthermore, a comparison to a reference with Baseline and ASHN model was carried out. CONCLUSIONS Attribute Selection Hybrid Network (ASHN) mimics the importance of attributes in social media posts to predict depressed mothers. Those mothers were anticipated to be depressed by answering a questionnaire designed by domain experts with prior knowledge of depression. This work will help researchers look at social media posts to find useful evidence for other depressive symptoms.
Collapse
Affiliation(s)
- Abinaya Gopalakrishnan
- School of Business, University of Southern Queensland, Springfield, QL, Australia.
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Chennai, India.
| | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, QL, Australia
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Chennai, India
| | - Revathi Venkataraman
- Department of Networking and Communications, School of Computing, SRM Institute of Science and Technology, Chennai, India
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, QL, Australia.
| | - Ka Chan Ching
- School of Business, University of Southern Queensland, Springfield, QL, Australia
| | - Arul Saravanan
- Department of Psychiatry, SRM Medical College Hospital & Research Centre, Chennai, India
| | - Maitrayee Sen
- Department of Obstetrics and Gynaecology, SRM Medical College Hospital & Research Centre, Chennai, India
| |
Collapse
|
13
|
Hawes MT, Schwartz HA, Son Y, Klein DN. Predicting adolescent depression and anxiety from multi-wave longitudinal data using machine learning. Psychol Med 2023; 53:6205-6211. [PMID: 36377499 DOI: 10.1017/s0033291722003452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND This study leveraged machine learning to evaluate the contribution of information from multiple developmental stages to prospective prediction of depression and anxiety in mid-adolescence. METHODS A community sample (N = 374; 53.5% male) of children and their families completed tri-annual assessments across ages 3-15. The feature set included several important risk factors spanning psychopathology, temperament/personality, family environment, life stress, interpersonal relationships, neurocognitive, hormonal, and neural functioning, and parental psychopathology and personality. We used canonical correlation analysis (CCA) to reduce the large feature set to a lower dimensional space while preserving the longitudinal structure of the data. Ablation analysis was conducted to evaluate the relative contributions to prediction of information gathered at different developmental periods and relative to previous disorder status (i.e. age 12 depression or anxiety) and demographics (sex, race, ethnicity). RESULTS CCA components from individual waves predicted age 15 disorder status better than chance across ages 3, 6, 9, and 12 for anxiety and 9 and 12 for depression. Only the components from age 12 for depression, and ages 9 and 12 for anxiety, improved prediction over prior disorder status and demographics. CONCLUSIONS These findings suggest that screening for risk of adolescent depression can be successful as early as age 9, while screening for risk of adolescent anxiety can be successful as early as age 3. Assessing additional risk factors at age 12 for depression, and going back to age 9 for anxiety, can improve screening for risk at age 15 beyond knowing standard demographics and disorder history.
Collapse
Affiliation(s)
- Mariah T Hawes
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - H Andrew Schwartz
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Youngseo Son
- Department of Computer Science, Stony Brook University, Stony Brook, NY, USA
| | - Daniel N Klein
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| |
Collapse
|
14
|
Nashwan AJ, Gharib S, Alhadidi M, El-Ashry AM, Alamgir A, Al-Hassan M, Khedr MA, Dawood S, Abufarsakh B. Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care. Issues Ment Health Nurs 2023; 44:1020-1034. [PMID: 37850937 DOI: 10.1080/01612840.2023.2263579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence in mental health. They also facilitate remote patient monitoring, bridge geographical gaps, and support clinical decision-making. The evolution of virtual mental health assistants and AI-enhanced therapeutic interventions are also discussed. These technological advancements reshape the nurse-patient interactions while ensuring personalized, efficient, and high-quality care. The review also addresses AI's ethical and responsible use in mental health nursing, emphasizing patient privacy, data security, and the balance between human interaction and AI tools. As AI applications in mental health care continue to evolve, this review encourages continued innovation while advocating for responsible implementation, thereby optimally leveraging the potential of AI in mental health nursing.
Collapse
Affiliation(s)
- Abdulqadir J Nashwan
- Nursing Department, Hamad Medical Corporation, Doha, Qatar
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Suzan Gharib
- Nursing Department, Al-Khaldi Hospital, Amman, Jordan
| | - Majdi Alhadidi
- Psychiatric & Mental Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan
| | | | | | | | | | - Shaimaa Dawood
- Faculty of Nursing, Alexandria University, Alexandria, Egypt
| | | |
Collapse
|
15
|
Wahid Z, Bari ASMH, Gavrilova M. Human Micro-Expressions in Multimodal Social Behavioral Biometrics. SENSORS (BASEL, SWITZERLAND) 2023; 23:8197. [PMID: 37837025 PMCID: PMC10575284 DOI: 10.3390/s23198197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/22/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023]
Abstract
The advent of Social Behavioral Biometrics (SBB) in the realm of person identification has underscored the importance of understanding unique patterns of social interactions and communication. This paper introduces a novel multimodal SBB system that integrates human micro-expressions from text, an emerging biometric trait, with other established SBB traits in order to enhance online user identification performance. Including human micro-expression, the proposed method extracts five other original SBB traits for a comprehensive representation of the social behavioral characteristics of an individual. Upon finding the independent person identification score by every SBB trait, a rank-level fusion that leverages the weighted Borda count is employed to fuse the scores from all the traits, obtaining the final identification score. The proposed method is evaluated on a benchmark dataset of 250 Twitter users, and the results indicate that the incorporation of human micro-expression with existing SBB traits can substantially boost the overall online user identification performance, with an accuracy of 73.87% and a recall score of 74%. Furthermore, the proposed method outperforms the state-of-the-art SBB systems.
Collapse
Affiliation(s)
- Zaman Wahid
- Biometric Technologies Laboratory, Department of Computer Science, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
| | | | | |
Collapse
|
16
|
Sinha GR, Larrison CR, Brooks I, Kursuncu U. Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter. JOURNAL OF EVIDENCE-BASED SOCIAL WORK (2019) 2023; 20:727-742. [PMID: 37461303 DOI: 10.1080/26408066.2023.2202668] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2023]
Abstract
PURPOSE The primary objective of this study was to identify patterns in users' naturalistic expressions on student loans on two social media platforms. The secondary objective was to examine how these patterns, sentiments, and emotions associated with student loans differ in user posts indicating mental illness. MATERIAL AND METHOD Data for this study were collected from Reddit and Twitter (2009-2020, n = 85,664) using certain key terms of student loans along with first-person pronouns as a triangulating measure of posts by individuals. Unsupervised and supervised machine learning models were used to analyze the text data. RESULTS Results suggested 50 topics in reddit finance and 40 each in reddit mental health communities and Twitter. Statistically significant associations were found between mental illness statuses and sentiments and emotions. Posts expressing mental illness showed more negative sentiments and were more likely to express sadness and fear. DISCUSSION AND CONCLUSION Patterns in social media discussions indicate both academic and non-academic consequences of having student debt, including users' desire to know more about their debts. Interventions should address the skill and information gaps between what is desired by the borrowers and what is offered to them in understanding and managing their debts. Cognitive burden created by student debts manifest itself on social media and can be used as an important marker to develop a nuanced understanding of people's expressions on a variety of socioeconomic issues. Higher volumes of negative sentiments and emotions of sadness, fear, and anger warrant immediate attention of policymakers and practitioners to reduce the cognitive burden of student debts.
Collapse
Affiliation(s)
- Gaurav R Sinha
- School of Social Work, University of Georgia, Athens, Georgia, USA
| | - Christopher R Larrison
- School of Social Work, University of Illinois at Urbana-Champaign, Urbana-Champaign, USA
| | - Ian Brooks
- Center for Health Informatics, The PAHO/WHO Collaborating Center on Information Systems for Health, and School of Information Sciences, University of Illinois at Urbana-Champaign, Urbana-Champaign, USA
| | - Ugur Kursuncu
- J. Mack Robinson College of Business, Georgia State University, Atlanta, USA
| |
Collapse
|
17
|
Foltz PW, Chandler C, Diaz-Asper C, Cohen AS, Rodriguez Z, Holmlund TB, Elvevåg B. Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function. Schizophr Res 2023; 259:127-139. [PMID: 36153250 DOI: 10.1016/j.schres.2022.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/12/2022] [Accepted: 07/13/2022] [Indexed: 11/23/2022]
Abstract
Modern advances in computational language processing methods have enabled new approaches to the measurement of mental processes. However, the field has primarily focused on model accuracy in predicting performance on a task or a diagnostic category. Instead the field should be more focused on determining which computational analyses align best with the targeted neurocognitive/psychological functions that we want to assess. In this paper we reflect on two decades of experience with the application of language-based assessment to patients' mental state and cognitive function by addressing the questions of what we are measuring, how it should be measured and why we are measuring the phenomena. We address the questions by advocating for a principled framework for aligning computational models to the constructs being assessed and the tasks being used, as well as defining how those constructs relate to patient clinical states. We further examine the assumptions that go into the computational models and the effects that model design decisions may have on the accuracy, bias and generalizability of models for assessing clinical states. Finally, we describe how this principled approach can further the goal of transitioning language-based computational assessments to part of clinical practice while gaining the trust of critical stakeholders.
Collapse
Affiliation(s)
- Peter W Foltz
- Institute of Cognitive Science, University of Colorado Boulder, United States of America.
| | - Chelsea Chandler
- Institute of Cognitive Science, University of Colorado Boulder, United States of America; Department of Computer Science, University of Colorado Boulder, United States of America
| | | | - Alex S Cohen
- Department of Psychology, Louisiana State University, United States of America; Center for Computation and Technology, Louisiana State University, United States of America
| | - Zachary Rodriguez
- Department of Psychology, Louisiana State University, United States of America; Center for Computation and Technology, Louisiana State University, United States of America
| | - Terje B Holmlund
- Department of Clinical Medicine, University of Tromsø - the Arctic University of Norway, Tromsø, Norway
| | - Brita Elvevåg
- Department of Clinical Medicine, University of Tromsø - the Arctic University of Norway, Tromsø, Norway; Norwegian Centre for eHealth Research, University Hospital of North Norway, Tromsø, Norway.
| |
Collapse
|
18
|
Mayor E, Bietti LM, Canales-Rodríguez EJ. Text as signal. A tutorial with case studies focusing on social media (Twitter). Behav Res Methods 2023; 55:2595-2620. [PMID: 35879505 PMCID: PMC9311346 DOI: 10.3758/s13428-022-01917-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/16/2022]
Abstract
Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.
Collapse
Affiliation(s)
- Eric Mayor
- Department of Psychology, Division of Clinical Psychology and Epidemiology, University of Basel, Basel, Switzerland.
| | - Lucas M Bietti
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | | |
Collapse
|
19
|
Zakariah M, Alotaibi YA. Unipolar and Bipolar Depression Detection and Classification Based on Actigraphic Registration of Motor Activity Using Machine Learning and Uniform Manifold Approximation and Projection Methods. Diagnostics (Basel) 2023; 13:2323. [PMID: 37510067 PMCID: PMC10377958 DOI: 10.3390/diagnostics13142323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/27/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Modern technology frequently uses wearable sensors to monitor many aspects of human behavior. Since continuous records of heart rate and activity levels are typically gathered, the data generated by these devices have a lot of promise beyond counting the number of daily steps or calories expended. Due to the patient's inability to obtain the necessary information to understand their conditions and detect illness, such as depression, objectively, methods for evaluating various mental disorders, such as the Montgomery-Asberg depression rating scale (MADRS) and observations, currently require a significant amount of effort on the part of specialists. In this study, a novel dataset was provided, comprising sensor data gathered from depressed patients. The dataset included 32 healthy controls and 23 unipolar and bipolar depressive patients with motor activity recordings. Along with the sensor data collected over several days of continuous measurement for each patient, some demographic information was also offered. The result of the experiment showed that less than 70 of the 100 epochs of the model's training were completed. The Cohen Kappa score did not even pass 0.1 in the validation set, due to an imbalance in the class distribution, whereas in the second experiment, the majority of scores peaked in about 20 epochs, but because training continued during each epoch, it took much longer for the loss to decline before it fell below 0.1. In the second experiment, the model soon reached an accuracy of 0.991, which is as expected given the outcome of the UMAP dimensionality reduction. In the last experiment, UMAP and neural networks worked together to produce the best outcomes. They used a variety of machine learning classification algorithms, including the nearest neighbors, linear kernel SVM, Gaussian process, and random forest. This paper used the UMAP unsupervised machine learning dimensionality reduction without the neural network and showed a slightly lower score (QDA). By considering the ratings of the patient's depressive symptoms that were completed by medical specialists, it is possible to better understand the relationship between depression and motor activity.
Collapse
Affiliation(s)
- Mohammed Zakariah
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh P.O. Box 11442, Saudi Arabia
| | - Yousef Ajami Alotaibi
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh P.O. Box 11451, Saudi Arabia
| |
Collapse
|
20
|
Meyerhoff J, Liu T, Stamatis CA, Liu T, Wang H, Meng Y, Curtis B, Karr CJ, Sherman G, Ungar LH, Mohr DC. Analyzing text message linguistic features: Do people with depression communicate differently with their close and non-close contacts? Behav Res Ther 2023; 166:104342. [PMID: 37269650 PMCID: PMC10330918 DOI: 10.1016/j.brat.2023.104342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 03/20/2023] [Accepted: 05/26/2023] [Indexed: 06/05/2023]
Abstract
BACKGROUND Relatively little is known about how communication changes as a function of depression severity and interpersonal closeness. We examined the linguistic features of outgoing text messages among individuals with depression and their close- and non-close contacts. METHODS 419 participants were included in this 16-week-long observational study. Participants regularly completed the PHQ-8 and rated subjective closeness to their contacts. Text messages were processed to count frequencies of word usage in the LIWC 2015 libraries. A linear mixed modeling approach was used to estimate linguistic feature scores of outgoing text messages. RESULTS Regardless of closeness, people with higher PHQ-8 scores tended to use more differentiation words. When texting with close contacts, individuals with higher PHQ-8 scores used more first-person singular, filler, sexual, anger, and negative emotion words. When texting with non-close contacts these participants used more conjunctions, tentative, and sadness-related words and fewer first-person plural words. CONCLUSION Word classes used in text messages, when combined with symptom severity and subjective social closeness data, may be indicative of underlying interpersonal processes. These data may hold promise as potential treatment targets to address interpersonal drivers of depression.
Collapse
Affiliation(s)
- Jonah Meyerhoff
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Tingting Liu
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA; Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Caitlin A Stamatis
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Tony Liu
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA; Roblox, San Mateo, CA, USA
| | - Harry Wang
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Yixuan Meng
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Brenda Curtis
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | | | - Garrick Sherman
- National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Lyle H Ungar
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - David C Mohr
- Department of Preventive Medicine, Center for Behavioral Intervention Technologies (CBITs), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| |
Collapse
|
21
|
Malik A, Shabaz M, Asenso E. Machine learning based model for detecting depression during Covid-19 crisis. SCIENTIFIC AFRICAN 2023; 20:e01716. [PMID: 37214195 PMCID: PMC10182866 DOI: 10.1016/j.sciaf.2023.e01716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/14/2023] [Accepted: 05/12/2023] [Indexed: 05/24/2023] Open
Abstract
Covid-19 has impacted negatively on people all over the world. Some of the ways that it has affected people include such as Health, Employment, Mental Health, Education, Social isolation, Economic Inequality and Access to healthcare and essential services. Apart from physical symptoms, it has caused considerable damage to mental health of individuals. Among all, depression is identified as one of the common illnesses which leads to early death. People suffering from depression are at a higher risk of developing other health conditions, such as heart disease and stroke, and are also at a higher risk of suicide. The importance of early detection and intervention of depression cannot be overstated. Identifying and treating depression early can prevent the illness from becoming more severe and can also prevent the development of other health conditions. Early detection can also prevent suicide, which is a leading cause of death among people with depression. Millions of people have affected from this disease. To proceed with the study of depression detection among individuals we have conducted a survey with 21 questions based on Hamilton tool and advise of psychiatrist. With the use of Python's scientific programming principles and machine learning methods like Decision Tree, KNN, and Naive Bayes, survey results were analysed. Further a comparison of these techniques is done. Study concludes that KNN has given better results than other techniques based on the accuracy and decision tree has given better results in the terms of latency to detect the depression of a person. At the conclusion, a machine learning-based model is suggested to replace the conventional method of detecting sadness by asking people encouraging questions and getting regular feedback from them.
Collapse
Affiliation(s)
- Arun Malik
- School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Mohammad Shabaz
- Model Institute of Engineering and Technology Jammu, J&K, India
| | - Evans Asenso
- Department of Agricultural Engineering, School of Engineering Sciences, University of Ghana, Accra, Ghana
| |
Collapse
|
22
|
Podina IR, Bucur AM, Todea D, Fodor L, Luca A, Dinu LP, Boian RF. Mental health at different stages of cancer survival: a natural language processing study of Reddit posts. Front Psychol 2023; 14:1150227. [PMID: 37425170 PMCID: PMC10326387 DOI: 10.3389/fpsyg.2023.1150227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Accepted: 06/07/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction The purpose of this study was to use text-based social media content analysis from cancer-specific subreddits to evaluate depression and anxiety-loaded content. Natural language processing, automatic, and lexicon-based methods were employed to perform sentiment analysis and identify depression and anxiety-loaded content. Methods Data was collected from 187 Reddit users who had received a cancer diagnosis, were currently undergoing treatment, or had completed treatment. Participants were split according to survivorship status into short-term, transition, and long-term cancer survivors. A total of 72524 posts were analyzed across the three cancer survivor groups. Results The results showed that short-term cancer survivors had significantly more depression-loaded posts and more anxiety-loaded words than long-term survivors, with no significant differences relative to the transition period. The topic analysis showed that long-term survivors, more than other stages of survivorship, have resources to share their experiences with suicidal ideation and mental health issues while providing support to their survivor community. Discussion The results indicate that Reddit texts seem to be an indicator of when the stressor is active and mental health issues are triggered. This sets the stage for Reddit to become a platform for screening and first-hand intervention delivery. Special attention should be dedicated to short-term survivors.
Collapse
Affiliation(s)
- Ioana R. Podina
- Laboratory of Cognitive Clinical Sciences, University of Bucharest, Bucharest, Romania
- Department of Applied Psychology, University of Bucharest, Bucharest, Romania
| | - Ana-Maria Bucur
- Interdisciplinary School of Doctoral Studies, University of Bucharest, Bucharest, Romania
| | - Diana Todea
- Interdisciplinary School of Doctoral Studies, University of Bucharest, Bucharest, Romania
| | - Liviu Fodor
- International Institute for The Advanced Studies of Psychotherapy and Applied Mental Health, Babeș-Bolyai University, Cluj-Napoca, Romania
- Evidence Based Psychological Assessment and Interventions Doctoral School, Babeș-Bolyai University, Cluj-Napoca, Romania
| | - Andreea Luca
- Interdisciplinary School of Doctoral Studies, University of Bucharest, Bucharest, Romania
| | - Liviu P. Dinu
- Human Language Technology Research Center, University of Bucharest, Bucharest, Romania
- Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania
| | - Rareș F. Boian
- Department of Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania
| |
Collapse
|
23
|
Stade EC, Ungar L, Havaldar S, Ruscio AM. Perseverative thinking is associated with features of spoken language. Behav Res Ther 2023; 165:104307. [PMID: 37121016 PMCID: PMC10263193 DOI: 10.1016/j.brat.2023.104307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 03/07/2023] [Accepted: 03/23/2023] [Indexed: 04/03/2023]
Abstract
Perseverative thinking (PT), such as rumination or worry, is a transdiagnostic process implicated in the onset and maintenance of emotional disorders. Existing measures of PT are limited by demand and expectancy effects, cognitive biases, and reflexivity, leading to calls for unobtrusive, behavioral measures. In response, we developed a behavioral measure of PT based on language. A mixed sample of 188 participants with major depressive disorder, generalized anxiety disorder, or no psychopathology completed self-report PT measures. Participants were also interviewed, providing a natural language sample. We examined language features associated with PT, then built a language-based PT model and examined its predictive power. PT was associated with multiple language features, most notably I-usage (e.g., "I", "me"; β = 0.25) and negative emotion language (e.g., "anxiety", "difficult"; β = 0.19). In machine learning analyses, language features accounted for 14% of the variance in self-reported PT. Language-based PT predicted the presence and severity of depression and anxiety, psychiatric comorbidity, and treatment seeking, with effects in the r = 0.15-0.41 range. PT has face-valid linguistic correlates and our language-based measure holds promise for assessing PT unobtrusively. With further development, this measure could be used to passively detect PT for deployment of "just-in-time" interventions.
Collapse
Affiliation(s)
- Elizabeth C Stade
- Department of Psychology, University of Pennsylvania, 425 South University Avenue, Philadelphia, PA, 19104-6018, USA.
| | - Lyle Ungar
- Department of Computer and Information Science, University of Pennsylvania, 504 Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104-6018, USA.
| | - Shreya Havaldar
- Department of Computer and Information Science, University of Pennsylvania, 504 Levine Hall, 3330 Walnut Street, Philadelphia, PA, 19104-6018, USA.
| | - Ayelet Meron Ruscio
- Department of Psychology, University of Pennsylvania, 425 South University Avenue, Philadelphia, PA, 19104-6018, USA.
| |
Collapse
|
24
|
Di Cara NH, Maggio V, Davis OSP, Haworth CMA. Methodologies for Monitoring Mental Health on Twitter: Systematic Review. J Med Internet Res 2023; 25:e42734. [PMID: 37155236 DOI: 10.2196/42734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/23/2022] [Accepted: 03/15/2023] [Indexed: 05/10/2023] Open
Abstract
BACKGROUND The use of social media data to predict mental health outcomes has the potential to allow for the continuous monitoring of mental health and well-being and provide timely information that can supplement traditional clinical assessments. However, it is crucial that the methodologies used to create models for this purpose are of high quality from both a mental health and machine learning perspective. Twitter has been a popular choice of social media because of the accessibility of its data, but access to big data sets is not a guarantee of robust results. OBJECTIVE This study aims to review the current methodologies used in the literature for predicting mental health outcomes from Twitter data, with a focus on the quality of the underlying mental health data and the machine learning methods used. METHODS A systematic search was performed across 6 databases, using keywords related to mental health disorders, algorithms, and social media. In total, 2759 records were screened, of which 164 (5.94%) papers were analyzed. Information about methodologies for data acquisition, preprocessing, model creation, and validation was collected, as well as information about replicability and ethical considerations. RESULTS The 164 studies reviewed used 119 primary data sets. There were an additional 8 data sets identified that were not described in enough detail to include, and 6.1% (10/164) of the papers did not describe their data sets at all. Of these 119 data sets, only 16 (13.4%) had access to ground truth data (ie, known characteristics) about the mental health disorders of social media users. The other 86.6% (103/119) of data sets collected data by searching keywords or phrases, which may not be representative of patterns of Twitter use for those with mental health disorders. The annotation of mental health disorders for classification labels was variable, and 57.1% (68/119) of the data sets had no ground truth or clinical input on this annotation. Despite being a common mental health disorder, anxiety received little attention. CONCLUSIONS The sharing of high-quality ground truth data sets is crucial for the development of trustworthy algorithms that have clinical and research utility. Further collaboration across disciplines and contexts is encouraged to better understand what types of predictions will be useful in supporting the management and identification of mental health disorders. A series of recommendations for researchers in this field and for the wider research community are made, with the aim of enhancing the quality and utility of future outputs.
Collapse
Affiliation(s)
- Nina H Di Cara
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | - Valerio Maggio
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Oliver S P Davis
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| | - Claire M A Haworth
- School of Psychological Science, University of Bristol, Bristol, United Kingdom
- The Alan Turing Institute, London, United Kingdom
| |
Collapse
|
25
|
Pool-Cen J, Carlos-Martínez H, Hernández-Chan G, Sánchez-Siordia O. Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation. Healthcare (Basel) 2023; 11:healthcare11071057. [PMID: 37046984 PMCID: PMC10094126 DOI: 10.3390/healthcare11071057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/18/2023] [Accepted: 03/20/2023] [Indexed: 04/08/2023] Open
Abstract
Mental health problems are one of the various ills that afflict the world’s population. Early diagnosis and medical care are public health problems addressed from various perspectives. Among the mental illnesses that most afflict the population is depression; its early diagnosis is vitally important, as it can trigger more severe illnesses, such as suicidal ideation. Due to the lack of homogeneity in current diagnostic tools, the community has focused on using AI tools for opportune diagnosis. Unfortunately, there is a lack of data that allows the use of IA tools for the Spanish language. Our work has a cross-lingual scheme to address this issue, allowing us to identify Spanish and English texts. The experiments demonstrated the methodology’s effectiveness with an F1-score of 0.95. With this methodology, we propose a method to solve a classification problem for depression tweets (or short texts) by reusing English language databases with insufficient data to generate a classification model, such as in the Spanish language. We also validated the information obtained with public data to analyze the behavior of depression in Mexico during the COVID-19 pandemic. Our results show that the use of these methodologies can serve as support, not only in the diagnosis of depression, but also in the construction of different language databases that allow the creation of more efficient diagnostic tools.
Collapse
Affiliation(s)
- Jorge Pool-Cen
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
| | - Hugo Carlos-Martínez
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
- IxM CONACyT, Mexico City 14240, Mexico
- Laboratorio Nacional de Geointeligencia (GeoInt), Mexico City 14240, Mexico
| | - Gandhi Hernández-Chan
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
- IxM CONACyT, Mexico City 14240, Mexico
- Laboratorio Nacional de Geointeligencia (GeoInt), Mexico City 14240, Mexico
| | - Oscar Sánchez-Siordia
- Geospatial Information Sciences Research Center, Mexico City 14240, Mexico
- Laboratorio Nacional de Geointeligencia (GeoInt), Mexico City 14240, Mexico
| |
Collapse
|
26
|
Inamdar S, Chapekar R, Gite S, Pradhan B. Machine Learning Driven Mental Stress Detection on Reddit Posts Using Natural Language Processing. HUMAN-CENTRIC INTELLIGENT SYSTEMS 2023. [PMCID: PMC10062685 DOI: 10.1007/s44230-023-00020-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
People’s mental conditions are often reflected in their social media activity due to the internet's anonymity. Psychiatric issues are often detected through such activities and can be addressed in their early stages, potentially preventing the consequences of unattended mental disorders like depression and anxiety. In this paper, the authors have implemented machine learning models and used various embedding techniques to classify posts from the famous social media blog site Reddit as stressful and non-stressful. The dataset used contains user posts that can be analyzed to detect patterns in the social media activity of those diagnosed with mental disorders. This paper uses different NLP (Natural Language Processing) tools such as ELMo (Embeddings from Language Models) word embeddings, BERT (Bidirectional Encoder Representations from Transformers) tokenizers, and BoW (Bag of Words) approach to create word/sentence data that can be fed to machine learning models. The results of each method have been discussed. The results achieved a top F1 score of 0.76, a Precision score of 0.71, and a Recall of 0.74 using only the preprocessed texts and machine learning algorithms to classify the posts. The results achieved by this paper are significant and have the potential to be applied in real-world scenarios to analyze mental stress among social media users. Although this paper focuses on data from Reddit, the techniques used can be transferred to similar social media platforms and could help solve the growing mental health crisis.
Collapse
Affiliation(s)
- Shaunak Inamdar
- grid.444681.b0000 0004 0503 4808AIML Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | - Rishikesh Chapekar
- grid.444681.b0000 0004 0503 4808AIML Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
| | - Shilpa Gite
- grid.444681.b0000 0004 0503 4808AIML Department, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India
- grid.444681.b0000 0004 0503 4808Symbiosis Center for Applied AI (SCAAI), Symbiosis International (Deemed University), Pune, India
| | - Biswajeet Pradhan
- grid.117476.20000 0004 1936 7611Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007 Australia
| |
Collapse
|
27
|
Owen D, Antypas D, Hassoulas A, Pardiñas AF, Espinosa-Anke L, Collados JC. Enabling Early Health Care Intervention by Detecting Depression in Users of Web-Based Forums using Language Models: Longitudinal Analysis and Evaluation. JMIR AI 2023; 2:e41205. [PMID: 37525646 PMCID: PMC7614849 DOI: 10.2196/41205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Background Major depressive disorder is a common mental disorder affecting 5% of adults worldwide. Early contact with health care services is critical for achieving accurate diagnosis and improving patient outcomes. Key symptoms of major depressive disorder (depression hereafter) such as cognitive distortions are observed in verbal communication, which can also manifest in the structure of written language. Thus, the automatic analysis of text outputs may provide opportunities for early intervention in settings where written communication is rich and regular, such as social media and web-based forums. Objective The objective of this study was 2-fold. We sought to gauge the effectiveness of different machine learning approaches to identify users of the mass web-based forum Reddit, who eventually disclose a diagnosis of depression. We then aimed to determine whether the time between a forum post and a depression diagnosis date was a relevant factor in performing this detection. Methods A total of 2 Reddit data sets containing posts belonging to users with and without a history of depression diagnosis were obtained. The intersection of these data sets provided users with an estimated date of depression diagnosis. This derived data set was used as an input for several machine learning classifiers, including transformer-based language models (LMs). Results Bidirectional Encoder Representations from Transformers (BERT) and MentalBERT transformer-based LMs proved the most effective in distinguishing forum users with a known depression diagnosis from those without. They each obtained a mean F1-score of 0.64 across the experimental setups used for binary classification. The results also suggested that the final 12 to 16 weeks (about 3-4 months) of posts before a depressed user's estimated diagnosis date are the most indicative of their illness, with data before that period not helping the models detect more accurately. Furthermore, in the 4- to 8-week period before the user's estimated diagnosis date, their posts exhibited more negative sentiment than any other 4-week period in their post history. Conclusions Transformer-based LMs may be used on data from web-based social media forums to identify users at risk for psychiatric conditions such as depression. Language features picked up by these classifiers might predate depression onset by weeks to months, enabling proactive mental health care interventions to support those at risk for this condition.
Collapse
Affiliation(s)
- David Owen
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
| | - Dimosthenis Antypas
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
| | - Athanasios Hassoulas
- Centre for Medical Education, School of Medicine, Cardiff
University, Cardiff, United Kingdom
| | - Antonio F Pardiñas
- Centre for Neuropsychiatric Genetics and Genomics, School of
Medicine, Cardiff University, Cardiff, United Kingdom
| | - Luis Espinosa-Anke
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
| | - Jose Camacho Collados
- School of Computer Science and Informatics, Cardiff University,
Cardiff, United Kingdom
| |
Collapse
|
28
|
Rivera CE, Kaunhoven RJ, Griffith GM. How an Interest in Mindfulness Influences Linguistic Markers in Online Microblogging Discourse. Mindfulness (N Y) 2023; 14:818-829. [PMID: 37090855 PMCID: PMC10020072 DOI: 10.1007/s12671-023-02098-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/24/2023] [Indexed: 03/19/2023]
Abstract
Objectives This study aimed to investigate the linguistic markers of an interest in mindfulness. Specifically, it examined whether individuals who follow mindfulness experts on Twitter use different language in their tweets compared to a random sample of Twitter users. This is a first step which may complement commonly used self-report measures of mindfulness with quantifiable behavioural metrics. Method A linguistic analysis examined the association between an interest in mindfulness and linguistic markers in 1.87 million Twitter entries across 19,732 users from two groups, (1) a mindfulness interest group (n = 10,347) comprising followers of five mindfulness experts and (2) a control group (n = 9385) of a random selection of Twitter users. Text analysis software (Linguistic Inquiry and Word Count) was used to analyse linguistic markers associated with the categories and subcategories of mindfulness, affective processes, social orientation, and “being” mode of mind. Results Analyses revealed an association between an interest in mindfulness and lexical choice. Specifically, tweets from the mindfulness interest group contained a significantly higher frequency of markers associated with mindfulness, positive emotion, happiness, and social orientation, and a significantly lower frequency of markers associated with negative emotion, past focus, present focus, future focus, family orientation, and friend orientation. Conclusions Results from this study suggest that an interest in mindfulness is associated with more frequent use of certain language markers on Twitter. The analysis opens possible pathways towards developing more naturalistic methods of understanding and assessing mindfulness which may complement self-reporting methods.
Collapse
Affiliation(s)
- Clara Eugenia Rivera
- Centre for Mindfulness Research and Practice, School of Human and Behavioural Sciences, Bangor University, Brigantia Building, Penrallt Road, Bangor, LL57 2AS UK
| | - Rebekah Jane Kaunhoven
- Centre for Mindfulness Research and Practice, School of Human and Behavioural Sciences, Bangor University, Brigantia Building, Penrallt Road, Bangor, LL57 2AS UK
| | - Gemma Maria Griffith
- Centre for Mindfulness Research and Practice, School of Human and Behavioural Sciences, Bangor University, Brigantia Building, Penrallt Road, Bangor, LL57 2AS UK
| |
Collapse
|
29
|
Robertson C, Carney J, Trudell S. Language about the future on social media as a novel marker of anxiety and depression: A big-data and experimental analysis. CURRENT RESEARCH IN BEHAVIORAL SCIENCES 2023. [DOI: 10.1016/j.crbeha.2023.100104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
|
30
|
Panicheva PV, Mamaev ID, Litvinova TA. Towards automatic conceptual metaphor detection for psychological tasks. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
|
31
|
Yao X, Chen S, Yu G. Effects of members’ response styles in an online depression community based on text mining and empirical analysis. Inf Process Manag 2023. [DOI: 10.1016/j.ipm.2022.103198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
|
32
|
Moulaei K, Iranmanesh E, Amiri P, Ahmadian L. Attitudes of Covid-19 patients toward sharing their health data: A survey-based study to understand security and privacy concerns. Health Sci Rep 2023; 6:e1132. [PMID: 36865528 PMCID: PMC9971706 DOI: 10.1002/hsr2.1132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/28/2023] [Accepted: 02/02/2023] [Indexed: 03/04/2023] Open
Abstract
Background and Aims Many people around the world, especially at the time of the Covid-19 outbreak, are concerned about their e-health data. The aim of this study was to investigate the attitudes of patients with Covid-19 toward sharing their health data for research and their concerns about security and privacy. Methods This survey is a cross-sectional study conducted through an electronic researcher-made questionnaire from February to May 2021. Convenience sampling was applied to select the participants and all 475 patients were referred to two to Afzalipour and Shahid Bahonar hospitals were invited to the study. According to the inclusion and exclusion criteria, 204 patients were included in the study and completed the questionnaire. Descriptive statistics (frequency, mean, and standard deviation) were used to analyze the questionnaire data. SPSS 23.0 was used for data analysis. Results Participants tended to share information about "comments provided by individuals on websites" (68.6%), "fitness tracker data" (64.19%), and "online shopping history" (63.21%) before death. Participants also tended to share information about "electronic medical records data" (36.75%), "genetic data" (24.99%), and "Instagram data" (24.99%) after death. "Fraud or misuse of personal information" (4.48 [±1.27]) was the most common concern of participants regarding the virtual world. "Unauthorized access to the account" (4.38 [±0.73]), "violation of the privacy of personal information" (4.26 [±0.85]), and "violation of the patient privacy and personal information confidentially" (4.26 [±0.85]) were the most of the unauthorized security incidents that occurred online for participants. Conclusion Patients with Covid-19 were concerned about releasing information they shared on websites and social networks. Therefore, people should be made aware of the reliability of websites and social media so that their security and privacy are not affected.
Collapse
Affiliation(s)
- Khadijeh Moulaei
- Student Research CommitteeKerman University of Medical SciencesKermanIran
| | - Elnaz Iranmanesh
- Department of Information Technoloy Engineering, Faculty of SciencesIslamic Azad UniversityKermanIran
| | - Parasto Amiri
- Student Research CommitteeKerman University of Medical SciencesKermanIran
| | - Leila Ahmadian
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran
| |
Collapse
|
33
|
Krishnamurti T, Allen K, Hayani L, Rodriguez S, Rothenberger S, Moses-Kolko E, Simhan H. Using natural language from a smartphone pregnancy app to identify maternal depression. RESEARCH SQUARE 2023:rs.3.rs-2583296. [PMID: 36865248 PMCID: PMC9980211 DOI: 10.21203/rs.3.rs-2583296/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Depression is highly prevalent in pregnancy, yet it often goes undiagnosed and untreated. Language can be an indicator of psychological well-being. This longitudinal, observational cohort study of 1,274 pregnancies examined written language shared in a prenatal smartphone app. Natural language feature of text entered in the app (e.g. in a journaling feature) throughout the course of participants' pregnancies were used to model subsequent depression symptoms. Language features were predictive of incident depression symptoms in a 30-day window (AUROC = 0.72) and offer insights into topics most salient in the writing of individuals experiencing those symptoms. When natural language inputs were combined with self-reported current mood, a stronger predictive model was produced (AUROC = 0.84). Pregnancy apps are a promising way to illuminate experiences contributing to depression symptoms. Even sparse language and simple patient-reports collected directly from these tools may support earlier, more nuanced depression symptom identification.
Collapse
|
34
|
Milintsevich K, Sirts K, Dias G. Towards automatic text-based estimation of depression through symptom prediction. Brain Inform 2023; 10:4. [PMID: 36780049 PMCID: PMC9925661 DOI: 10.1186/s40708-023-00185-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/18/2023] [Indexed: 02/14/2023] Open
Abstract
Major Depressive Disorder (MDD) is one of the most common and comorbid mental disorders that impacts a person's day-to-day activity. In addition, MDD affects one's linguistic footprint, which is reflected by subtle changes in speech production. This allows us to use natural language processing (NLP) techniques to build a neural classifier to detect depression from speech transcripts. Typically, current NLP systems discriminate only between the depressed and non-depressed states. This approach, however, disregards the complexity of the clinical picture of depression, as different people with MDD can suffer from different sets of depression symptoms. Therefore, predicting individual symptoms can provide more fine-grained information about a person's condition. In this work, we look at the depression classification problem through the prism of the symptom network analysis approach, which shifts attention from a categorical analysis of depression towards a personalized analysis of symptom profiles. For that purpose, we trained a multi-target hierarchical regression model to predict individual depression symptoms from patient-psychiatrist interview transcripts from the DAIC-WOZ corpus. Our model achieved results on par with state-of-the-art models on both binary diagnostic classification and depression severity prediction while at the same time providing a more fine-grained overview of individual symptoms for each person. The model achieved a mean absolute error (MAE) from 0.438 to 0.830 on eight depression symptoms and showed state-of-the-art results in binary depression estimation (73.9 macro-F1) and total depression score prediction (3.78 MAE). Moreover, the model produced a symptom correlation graph that is structurally identical to the real one. The proposed symptom-based approach provides more in-depth information about the depressive condition by focusing on the individual symptoms rather than a general binary diagnosis.
Collapse
Affiliation(s)
- Kirill Milintsevich
- Institute of Computer Science, University of Tartu, Tartu, Estonia. .,Groupe de Recherche en Informatique, Image et Instrumentation (GREYC), National Graduate School of Engineering and Research Center (ENSICAEN), Université de Caen Normandie (UNICAEN), 14000, Caen, France.
| | - Kairit Sirts
- grid.10939.320000 0001 0943 7661Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Gaël Dias
- grid.412043.00000 0001 2186 4076Groupe de Recherche en Informatique, Image et Instrumentation (GREYC), National Graduate School of Engineering and Research Center (ENSICAEN), Université de Caen Normandie (UNICAEN), 14000 Caen, France
| |
Collapse
|
35
|
Zhu J, Li Z, Zhang X, Zhang Z, Hu B. Public attitudes towards anxiety disorder on Sina Weibo: content analysis (Preprint). J Med Internet Res 2023; 25:e45777. [PMID: 37014691 PMCID: PMC10131780 DOI: 10.2196/45777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/07/2023] [Accepted: 03/08/2023] [Indexed: 03/10/2023] Open
Abstract
BACKGROUND Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. OBJECTIVE The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. METHODS From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword "anxiety disorder" were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. RESULTS The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area "discrimination and stigma" reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area "family and life" (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. CONCLUSIONS The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.
Collapse
Affiliation(s)
- Jianghong Zhu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zepeng Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiu Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhenwen Zhang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| |
Collapse
|
36
|
Thati RP, Dhadwal AS, Kumar P, P S. A novel multi-modal depression detection approach based on mobile crowd sensing and task-based mechanisms. MULTIMEDIA TOOLS AND APPLICATIONS 2023; 82:4787-4820. [PMID: 35431608 PMCID: PMC9000000 DOI: 10.1007/s11042-022-12315-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 09/20/2021] [Accepted: 01/17/2022] [Indexed: 05/05/2023]
Abstract
Depression has become a global concern, and COVID-19 also has caused a big surge in its incidence. Broadly, there are two primary methods of detecting depression: Task-based and Mobile Crowd Sensing (MCS) based methods. These two approaches, when integrated, can complement each other. This paper proposes a novel approach for depression detection that combines real-time MCS and task-based mechanisms. We aim to design an end-to-end machine learning pipeline, which involves multimodal data collection, feature extraction, feature selection, fusion, and classification to distinguish between depressed and non-depressed subjects. For this purpose, we created a real-world dataset of depressed and non-depressed subjects. We experimented with: various features from multi-modalities, feature selection techniques, fused features, and machine learning classifiers such as Logistic Regression, Support Vector Machines (SVM), etc. for classification. Our findings suggest that combining features from multiple modalities perform better than any single data modality, and the best classification accuracy is achieved when features from all three data modalities are fused. Feature selection method based on Pearson's correlation coefficients improved the accuracy in comparison with other methods. Also, SVM yielded the best accuracy of 86%. Our proposed approach was also applied on benchmarking dataset, and results demonstrated that the multimodal approach is advantageous in performance with state-of-the-art depression recognition techniques.
Collapse
Affiliation(s)
- Ravi Prasad Thati
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440010 Maharashtra India
| | - Abhishek Singh Dhadwal
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440010 Maharashtra India
| | - Praveen Kumar
- Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur, 440010 Maharashtra India
| | - Sainaba P
- Department of Applied Psychology, Central University of Tamil Nadu, Tamilnadu, India
| |
Collapse
|
37
|
Garg M. Mental Health Analysis in Social Media Posts: A Survey. ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2023; 30:1819-1842. [PMID: 36619138 PMCID: PMC9810253 DOI: 10.1007/s11831-022-09863-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Accepted: 11/05/2022] [Indexed: 05/21/2023]
Abstract
The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of social media data bring to fore the importance of real-time responsible AI models for mental health analysis. Aiming to classify the research directions for social computing and tracking advances in the development of machine learning (ML) and deep learning (DL) based models, we propose a comprehensive survey on quantifying mental health on social media. We compose a taxonomy for mental healthcare and highlight recent attempts in examining social well-being with personal writings on social media. We define all the possible research directions for mental healthcare and investigate a thread of handling online social media data for stress, depression and suicide detection for this work. The key features of this manuscript are (i) feature extraction and classification, (ii) recent advancements in AI models, (iii) publicly available dataset, (iv) new frontiers and future research directions. We compile this information to introduce young research and academic practitioners with the field of computational intelligence for mental health analysis on social media. In this manuscript, we carry out a quantitative synthesis and a qualitative review with the corpus of over 92 potential research articles. In this context, we release the collection of existing work on suicide detection in an easily accessible and updatable repository:https://github.com/drmuskangarg/mentalhealthcare.
Collapse
Affiliation(s)
- Muskan Garg
- University of Florida, Gainesville, FL 32601 USA
| |
Collapse
|
38
|
Koops S, Brederoo SG, de Boer JN, Nadema FG, Voppel AE, Sommer IE. Speech as a Biomarker for Depression. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:152-160. [PMID: 34961469 DOI: 10.2174/1871527320666211213125847] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/10/2021] [Accepted: 10/10/2021] [Indexed: 01/01/2023]
Abstract
BACKGROUND Depression is a debilitating disorder that at present lacks a reliable biomarker to aid in diagnosis and early detection. Recent advances in computational analytic approaches have opened up new avenues in developing such a biomarker by taking advantage of the wealth of information that can be extracted from a person's speech. OBJECTIVE The current review provides an overview of the latest findings in the rapidly evolving field of computational language analysis for the detection of depression. We cover a wide range of both acoustic and content-related linguistic features, data types (i.e., spoken and written language), and data sources (i.e., lab settings, social media, and smartphone-based). We put special focus on the current methodological advances with regard to feature extraction and computational modeling techniques. Furthermore, we pay attention to potential hurdles in the implementation of automatic speech analysis. CONCLUSION Depressive speech is characterized by several anomalies, such as lower speech rate, less pitch variability and more self-referential speech. With current computational modeling techniques, such features can be used to detect depression with an accuracy of up to 91%. The performance of the models is optimized when machine learning techniques are implemented that suit the type and amount of data. Recent studies now work towards further optimization and generalizability of the computational language models to detect depression. Finally, privacy and ethical issues are of paramount importance to be addressed when automatic speech analysis techniques are further implemented in, for example, smartphones. Altogether, computational speech analysis is well underway towards becoming an effective diagnostic aid for depression.
Collapse
Affiliation(s)
- Sanne Koops
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Sanne G Brederoo
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
- University Center for Psychiatry, University Medical Center Groningen, Groningen, The Netherlands
| | - Janna N de Boer
- Department of Psychiatry, University Medical Center Utrecht, Utrecht University & Brain Center Rudolf Magnus, Utrecht, The Netherlands
| | - Femke G Nadema
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Alban E Voppel
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neurosciences, University of Groningen, University Medical Center Groningen (UMCG), Groningen, The Netherlands
| |
Collapse
|
39
|
Gong F, Lei Z, Min H, Yu Y, Huang Z, Liu J, Wu W, Tang J, Sun X, Wu Y. Can smartphone use affect chronic disease self-management among Chinese middle-aged and older adults? A moderated mediation model. Front Psychol 2022; 13:1019335. [PMID: 36619052 PMCID: PMC9815028 DOI: 10.3389/fpsyg.2022.1019335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Chronic disease self-management is influenced by many factors. Previous studies have linked patients' media use with chronic disease self-management, but the underlying mechanisms of this relationship are less understood. Objectives The purpose of this study is to explore the mediating role of family health (FH) between frequency of smartphone use (FOSU) and self-management behaviors among middle-aged and older patients with chronic diseases (SBAMAOPWCD) through a moderated mediation model, and whether this indirect relationship is modified by the solitary status of middle-aged and older Chinese patients with chronic disease. Methods Surveys were collected from 1,424 (N = 1,424; age > 45) middle-aged and older with one or more chronic conditions in China on self-reports of FOSU, FH and Chronic disease self-management behaviors were used to examine the moderated mediation model. Results The results showed that the FOSU was significantly and positively associated with SBAMAOPWCD (β = 0.220, p < 0.001; β = 0.170, p < 0.001; β = 0.167, p < 0.001; β = 0.158, p < 0.001); The Family health resources (FHR) dimension of FH and the Family external social supports (FESS) dimension mediated the relationship between the FOSU and SBAMAOPWCD (β = -0.0758, CI: -0.1402, -0.0236; β = 0.0721, CI: 0.0141, 0.1458), Among them, the FHR dimension mediated mainly among FOSU, exercise and cognitive symptom management practices (CSMP; β = -0.0344, CI: -0.0652, -0.0102; β = -0.0401, CI: -0.0725, -0.0138), the FESS dimension of the FH mediated the relationship between the FOSU and communication with physicians (CWP; β = 0.0376, CI: 0.0116, 0.0705); Solitary state played a moderating role in the relationship between FHR dimension and SBAMAOPWCD (live alone β = -0.2395, CI: -0.4574, -0.0661; not live-alone β = -0.0599, CI: -0.1164, -0.0172). In addition, solitary state played a moderating role in the relationship among FHR dimension and CSMP for middle-aged and older patients (live alone β = -0.1095, CI: -0.1961, -0.0378; not live-alone β = -0.0334, CI: -0.0633, -0.0102). Interestingly, the relationship between FESS dimension and SBAMAOPWCD was moderated only by the non-live alone population (β = 0.0676, CI: 0.0008, 0.1478), and not by the live-alone population (β = 0.1026, CI: -0.1061, 0.3278).Unexpectedly, we found that when their FHR were lower, they reported higher levels of chronic disease self-management, middle-aged and older patients with chronic diseases who live alone are more significant in this impact relationship. Conclusions The study further deepens our understanding of the mechanisms linking frequency of smartphone use with chronic disease self-management behaviors, and it helps to develop interventions to improve chronic disease self-management behaviors in middle-aged and older adults.
Collapse
Affiliation(s)
- Fangmin Gong
- School of Literature and Journalism Communication, Jishou University, Jishou, China
| | - Zhaowen Lei
- School of Literature and Journalism Communication, Jishou University, Jishou, China,Zhaowen Lei,
| | - Hewei Min
- School of Public Health, Peking University, Beijing, China
| | - Yebo Yu
- School of Public Health, Peking University, Beijing, China
| | - Zhen Huang
- School of Public Health, Peking University, Beijing, China
| | - Jingyao Liu
- School of Public Health, Shandong University, Jinan, Shandong Province, China
| | - Wenyu Wu
- School of Health Management, Harbin Medical University, Harbin, Heilongjiang Province, China
| | - Jingqi Tang
- School of Philosophy, Anhui University, Hefei, Anhui Province, China
| | - Xinying Sun
- School of Public Health, Peking University, Beijing, China
| | - Yibo Wu
- School of Public Health, Peking University, Beijing, China,*Correspondence: Yibo Wu,
| |
Collapse
|
40
|
Mayor E, Miché M, Lieb R. Associations between emotions expressed in internet news and subsequent emotional content on twitter. Heliyon 2022; 8:e12133. [PMID: 36561692 PMCID: PMC9763764 DOI: 10.1016/j.heliyon.2022.e12133] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 10/27/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
We report on the first investigation of large-scale temporal associations between emotions expressed in online news media and those expressed on social media (Twitter). This issue has received little attention in previous research, although the study of emotions expressed on social media has bloomed owing to its importance in the study of mental health at the population level. Relying on automatically emotion-coded data from almost 1 million online news articles on disease and the coronavirus and more than 6 million tweets, we examined such associations. We found that prior changes in generic emotional categories (positive and negative emotions) in the news on the topic of disease were associated with lagged changes in these categories in tweets. Discrete negative emotions did not robustly feature this pattern. Emotional categories coded in online news stories on the coronavirus generally featured weaker and more disparate lagged associations with emotional categories coded in subsequent tweets.
Collapse
|
41
|
Stamatis CA, Meyerhoff J, Liu T, Sherman G, Wang H, Liu T, Curtis B, Ungar LH, Mohr DC. Prospective associations of text-message-based sentiment with symptoms of depression, generalized anxiety, and social anxiety. Depress Anxiety 2022; 39:794-804. [PMID: 36281621 PMCID: PMC9729432 DOI: 10.1002/da.23286] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/16/2022] [Accepted: 10/02/2022] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE Language patterns may elucidate mechanisms of mental health conditions. To inform underlying theory and risk models, we evaluated prospective associations between in vivo text messaging language and differential symptoms of depression, generalized anxiety, and social anxiety. METHODS Over 16 weeks, we collected outgoing text messages from 335 adults. Using Linguistic Inquiry and Word Count (LIWC), NRC Emotion Lexicon, and previously established depression and stress dictionaries, we evaluated the degree to which language features predict symptoms of depression, generalized anxiety, or social anxiety the following week using hierarchical linear models. To isolate the specificity of language effects, we also controlled for the effects of the two other symptom types. RESULTS We found significant relationships of language features, including personal pronouns, negative emotion, cognitive and biological processes, and informal language, with common mental health conditions, including depression, generalized anxiety, and social anxiety (ps < .05). There was substantial overlap between language features and the three mental health outcomes. However, after controlling for other symptoms in the models, depressive symptoms were uniquely negatively associated with language about anticipation, trust, social processes, and affiliation (βs: -.10 to -.09, ps < .05), whereas generalized anxiety symptoms were positively linked with these same language features (βs: .12-.13, ps < .001). Social anxiety symptoms were uniquely associated with anger, sexual language, and swearing (βs: .12-.13, ps < .05). CONCLUSION Language that confers both common (e.g., personal pronouns and negative emotion) and specific (e.g., affiliation, anticipation, trust, and anger) risk for affective disorders is perceptible in prior week text messages, holding promise for understanding cognitive-behavioral mechanisms and tailoring digital interventions.
Collapse
Affiliation(s)
- Caitlin A. Stamatis
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Jonah Meyerhoff
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Tingting Liu
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Garrick Sherman
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Harry Wang
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Tony Liu
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- RobloxSan MateoCaliforniaUSA
| | - Brenda Curtis
- Technology & Translational Research Unit, National Institute on Drug Abuse (NIDA IRP)National Institutes of Health (NIH)BaltimoreMarylandUSA
| | - Lyle H. Ungar
- Positive Psychology CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Computer and Information ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David C. Mohr
- Center for Behavioral Intervention TechnologiesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| |
Collapse
|
42
|
Kruzan KP, Williams KD, Meyerhoff J, Yoo DW, O'Dwyer LC, De Choudhury M, Mohr DC. Social media-based interventions for adolescent and young adult mental health: A scoping review. Internet Interv 2022; 30:100578. [PMID: 36204674 PMCID: PMC9530477 DOI: 10.1016/j.invent.2022.100578] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 09/18/2022] [Accepted: 09/27/2022] [Indexed: 11/18/2022] Open
Abstract
Background Mental health conditions are common among adolescents and young adults, yet few receive adequate mental health treatment. Many young people seek support and information online through social media, and report preferences for digital interventions. Thus, digital interventions deployed through social media have promise to reach a population not yet engaged in treatment, and at risk of worsening symptoms. Objective In this scoping review, we aimed to identify and review empirical research on social media-based interventions aimed at improving adolescent and young adult mental health. A secondary objective was to identify the features and functionalities of platforms described as social media. Methods Adhering to the PRISMA-ScR guidelines for scoping reviews, the search was conducted in PubMed MEDLINE; Embase Central Register of Controlled Trials (Wiley); PsycINFO (Ebsco); Scopus; Web of Science; IEEE Xplore; ACM Digital Library; and ClinicalTrials.gov from inception until November 2021. Studies were included if they involved adolescents or young adults (10-26 years of age) that meet clinical, or subclinical, levels of a mental health condition and include a pre- and post-assessment of mental health outcomes. Results Among the 18,380 references identified, 15 met full inclusion criteria and were published between 2017 and 2021-this included four randomized controlled trials, seven non-randomized pre-post trials, and four were experimental or quasi-experimental designs. Just five studies were delivered through an existing social media site (Facebook or Pixtori), with the remainder focused on purpose-built networks. Three studies involved adolescents or young adults who self-reported a mental health condition, seven involved young people diagnosed with a mental health condition by a clinician or who scored above a clinical threshold on valid clinical measure, three involved college students without a mental health inclusion criterion, and two studies focused on young people with a cancer diagnosis. Conclusions The review highlights innovations in the delivery of mental health interventions, provides preliminary evidence of the ability of social media interventions to improve mental health outcomes, and underscores the need for, and merit of, future work in this area. We discuss opportunities and challenges for future research, including the potential to leveragei existing peer networks, the use of just-in-time interventions, and scaling interventions to meet need.
Collapse
Affiliation(s)
- Kaylee Payne Kruzan
- Center for Behavioral Intervention Technologies, Feinberg School of Medicine, Northwestern University, 750 N. Lakeshore Drive, Chicago, IL 60611, USA
| | - Kofoworola D.A. Williams
- Center for Behavioral Intervention Technologies, Feinberg School of Medicine, Northwestern University, 750 N. Lakeshore Drive, Chicago, IL 60611, USA
| | - Jonah Meyerhoff
- Center for Behavioral Intervention Technologies, Feinberg School of Medicine, Northwestern University, 750 N. Lakeshore Drive, Chicago, IL 60611, USA
| | - Dong Whi Yoo
- School of Interactive Computing, Georgia Institute of Technology, 85 5th St NW, Atlanta, GA 30308, USA
| | - Linda C. O'Dwyer
- Center for Behavioral Intervention Technologies, Feinberg School of Medicine, Northwestern University, 750 N. Lakeshore Drive, Chicago, IL 60611, USA
| | - Munmun De Choudhury
- School of Interactive Computing, Georgia Institute of Technology, 85 5th St NW, Atlanta, GA 30308, USA
| | - David C. Mohr
- Center for Behavioral Intervention Technologies, Feinberg School of Medicine, Northwestern University, 750 N. Lakeshore Drive, Chicago, IL 60611, USA
| |
Collapse
|
43
|
Hegazi O, Alalalmeh S, Alfaresi A, Dashtinezhad S, Bahada A, Shahwan M, Jairoun AA, Babalola TK, Yasin H. Development, Validation, and Utilization of a Social Media Use and Mental Health Questionnaire among Middle Eastern and Western Adults: A Pilot Study from the UAE. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16063. [PMID: 36498139 PMCID: PMC9736958 DOI: 10.3390/ijerph192316063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/20/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVES We aimed to develop and validate a mental health stigma measurement tool for use within the social media context, utilizing the tool to assess whether the stigma shown in face-to-face interactions translates to social media, coupled with comparing whether social media use can cause the stigma among a sample of Middle Eastern and Western populations. METHODS The development and validation phase comprised a systematic process that was used to develop an assessment tool that could be used within the social media context and establish its validity and reliability. A 5-point Likert-type scale (1 = strongly disagree to 5 = strongly agree) was developed to assess mental health stigma. The anonymous questionnaire was distributed from June 2022 to August 2022 on various social media platforms and groups predominated by the two demographics of interest, enrolling 1328 participants (with only 1001 responses deemed valid). The utilization phase consisted of bivariate and multivariable analysis of the data. The cutoff points for low, medium, and high scores were the 25th, 50th, and 75th percentil, respectively. RESULTS The instrument comprised three dimensions: acceptance, intolerance, and digital care sentiment. In the Middle Eastern subset of participants, a higher score of intolerance (more stigma) toward mental illness was found in 72.4% of the participants, with a higher score of acceptance being 35.1% and of digital care sentiment being 46.4%. The mean scores for all the scales were as follows: intolerance (3.08 ± 0.64), acceptance (3.87 ± 0.71), and digital care sentiment (3.18 ± 0.69). For Westerners, a higher score of intolerance toward mental illness was found in 24.0% of the participants, with a higher score of acceptance being 56.8% and of digital care sentiment being 38.2%. The mean scores for all the scales were as follows: intolerance (2.28 ± 0.73), acceptance (4.21 ± 0.61), and digital care sentiment (3.08 ± 0.62). Various results were obtained regarding the effect of individual social media platforms on the different subscales. CONCLUSIONS Stigma does follow people on social media, whether they are Middle Easterners or Westerners, although to varying degrees. The results of social media interaction and activity varied based on the group that used them, with some having an impact on one group but not the other. For these reasons, proper guidance is advised when utilizing and interacting with social media platforms.
Collapse
Affiliation(s)
- Omar Hegazi
- College of Pharmacy and Health Sciences, Ajman University, Ajman 346, United Arab Emirates
| | - Samer Alalalmeh
- College of Pharmacy and Health Sciences, Ajman University, Ajman 346, United Arab Emirates
| | - Ahmad Alfaresi
- College of Pharmacy and Health Sciences, Ajman University, Ajman 346, United Arab Emirates
| | - Soheil Dashtinezhad
- College of Pharmacy and Health Sciences, Ajman University, Ajman 346, United Arab Emirates
| | - Ahmed Bahada
- College of Engineering, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Moyad Shahwan
- College of Pharmacy and Health Sciences, Ajman University, Ajman 346, United Arab Emirates
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman 346, United Arab Emirates
| | | | - Tesleem K. Babalola
- Program in Public Health, Renaissance School of Medicine, Stony Brook University, Stony Brook, NY 11794, USA
| | - Haya Yasin
- College of Pharmacy and Health Sciences, Ajman University, Ajman 346, United Arab Emirates
- Centre of Medical and Bio-allied Health Sciences Research, Ajman University, Ajman 346, United Arab Emirates
| |
Collapse
|
44
|
Barua PD, Vicnesh J, Lih OS, Palmer EE, Yamakawa T, Kobayashi M, Acharya UR. Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review. Cogn Neurodyn 2022:1-22. [PMID: 36467993 PMCID: PMC9684805 DOI: 10.1007/s11571-022-09904-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 09/26/2022] [Accepted: 10/17/2022] [Indexed: 11/24/2022] Open
Abstract
Epidemiological studies report high levels of anxiety and depression amongst adolescents. These psychiatric conditions and complex interplays of biological, social and environmental factors are important risk factors for suicidal behaviours and suicide, which show a peak in late adolescence and early adulthood. Although deaths by suicide have fallen globally in recent years, suicide deaths are increasing in some countries, such as the US. Suicide prevention is a challenging global public health problem. Currently, there aren't any validated clinical biomarkers for suicidal diagnosis, and traditional methods exhibit limitations. Artificial intelligence (AI) is budding in many fields, including in the diagnosis of medical conditions. This review paper summarizes recent studies (past 8 years) that employed AI tools for the automated detection of depression and/or anxiety disorder and discusses the limitations and effects of some modalities. The studies assert that AI tools produce promising results and could overcome the limitations of traditional diagnostic methods. Although using AI tools for suicidal ideation exhibits limitations, these are outweighed by the advantages. Thus, this review article also proposes extracting a fusion of features such as facial images, speech signals, and visual and clinical history features from deep models for the automated detection of depression and/or anxiety disorder in individuals, for future work. This may pave the way for the identification of individuals with suicidal thoughts.
Collapse
Affiliation(s)
- Prabal Datta Barua
- School of Management and Enterprise, University of Southern Queensland, Springfield, Australia
| | - Jahmunah Vicnesh
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Oh Shu Lih
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
| | - Elizabeth Emma Palmer
- Discipline of Pediatric and Child Health, School of Clinical Medicine, University of New South Wales, Kensington, Australia
- Sydney Children’s Hospitals Network, Sydney, Australia
| | - Toshitaka Yamakawa
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Makiko Kobayashi
- Department of Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, Japan
| | - Udyavara Rajendra Acharya
- Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore
- School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taizhong, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan
| |
Collapse
|
45
|
Tejaswini V, Babu KS, Sahoo B. Depression Detection from Social Media Text Analysis using Natural Language Processing Techniques and Hybrid Deep Learning Model. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3569580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Depression is a kind of emotion that negatively impacts people's daily lives. The number of people suffering from long-term feelings is increasing every year across the globe. Depressed patients may engage in self-harm behaviors, which occasionally result in suicide. Many psychiatrists struggle to identify the presence of mental illness or negative emotion early to provide a better course of treatment before they reach a critical stage. One of the most challenging problems is detecting depression in people at the earliest possible stage. Researchers are using Natural Language Processing (NLP) techniques to analyze text content uploaded on social media, which helps to design approaches for detecting depression. This work analyses numerous prior studies that used learning techniques to identify depression. The existing methods suffer from better model representation problems to detect depression from the text with high accuracy. The present work addresses a solution to these problems by creating a new hybrid deep learning neural network design with better text representations called "Fasttext Convolution Neural Network with Long Short-Term Memory (FCL)." In addition, this work utilizes the advantage of NLP to simplify the text analysis during the model development. The FCL model comprises fasttext embedding for better text representation considering out-of-vocabulary (OOV) with semantic information, a convolution neural network (CNN) architecture to extract global information, and Long Short-Term Memory (LSTM) architecture to extract local features with dependencies. The present work was implemented on real-world datasets utilized in the literature. The proposed technique provides better results than the state-of-the-art to detect depression with high accuracy.
Collapse
Affiliation(s)
- Vankayala Tejaswini
- Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, India
| | - Korra Sathya Babu
- Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing, Kurnool, Andhra Pradesh, India
| | - Bibhudatta Sahoo
- Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, India
| |
Collapse
|
46
|
Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
47
|
Abu-Taieh EM, AlHadid I, Masa’deh R, Alkhawaldeh RS, Khwaldeh S, Alrowwad A. Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph192113764. [PMID: 36360644 PMCID: PMC9656283 DOI: 10.3390/ijerph192113764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/14/2022] [Accepted: 10/17/2022] [Indexed: 05/12/2023]
Abstract
Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents' depression and anxiety constructs as mediators that may account for children's depression and anxiety. The purpose of this paper is to test the influence of different factors on children's depression and anxiety, extending from parents' anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent's anxiety and depression which correlate to their offspring's anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent-child anxiety and depression due to the use of social networks.
Collapse
Affiliation(s)
- Evon M. Abu-Taieh
- Department of Computer Information Systems, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
| | - Issam AlHadid
- Department Information Technology, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
| | - Ra’ed Masa’deh
- Department of Management Information Systems, School of Business, The University of Jordan, Amman 77110, Jordan
| | - Rami S. Alkhawaldeh
- Department of Computer Information Systems, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
| | - Sufian Khwaldeh
- Department Information Technology, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
- Department Information Technology, Faculty of Information Technology and Systems, University of Fujairah, Fujairah P.O. Box 2202, United Arab Emirates
| | - Ala’aldin Alrowwad
- Department of Business Management, School of Business, The University of Jordan, Aqaba 77110, Jordan
- Correspondence:
| |
Collapse
|
48
|
Liu T, Ungar LH, Curtis B, Sherman G, Yadeta K, Tay L, Eichstaedt JC, Guntuku SC. Head versus heart: social media reveals differential language of loneliness from depression. NPJ MENTAL HEALTH RESEARCH 2022; 1:16. [PMID: 38609477 PMCID: PMC10955894 DOI: 10.1038/s44184-022-00014-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/12/2022] [Indexed: 04/14/2024]
Abstract
We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression).
Collapse
Affiliation(s)
- Tingting Liu
- National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA.
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA.
| | - Lyle H Ungar
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Brenda Curtis
- National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Garrick Sherman
- Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Kenna Yadeta
- National Institute on Drug Abuse (NIDA IRP), National Institutes of Health (NIH), Baltimore, MD, USA
| | - Louis Tay
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Johannes C Eichstaedt
- Department of Psychology, Institute for Human-Centered A.I., Stanford University, Stanford, CA, USA
| | - Sharath Chandra Guntuku
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
49
|
Gong F, Lei Z, Gong Z, Min H, Ge P, Guo Y, Ming WK, Sun X, Wu Y. The Role of Family Health in Mediating the Association between Smartphone Use and Health Risk Behaviors among Chinese Adolescent Students: A National Cross-Sectional Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:13378. [PMID: 36293956 PMCID: PMC9603663 DOI: 10.3390/ijerph192013378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
The direct impact of smartphones on health risk behaviors of adolescent students has been verified. However, the mediating mechanisms that underly this relationship remain largely unknown. Therefore, the aim of the study is to explore the role of family health in mediating the relationship between the frequency of smartphone use and adolescent students' health risk behaviors. A questionnaire was used to collect cross-sectional data from 693 adolescent students aged 12-18 in China and a structural equation model was analyzed. Among the nine health risk behaviors, the most frequent health risk behaviors in Chinese adolescent students were non-compliance walking behaviors (M=Mean; SD = Standard deviation) (M ± SD) (2.78 ± 1.747), eating unhygienic food (M ± SD) (2.23 ± 1.299), being subjected to physical violence (M ± SD) (2.19 ± 0.645), and leaving home (M ± SD) (2.13 ± 0.557). The SEM results showed that the adolescent students' smartphone use had a positive impact on delaying the age of first alcohol consumption (β = 0.167, CI:0.067 0.287) and a negative impact on the non-compliance walking behaviors (β = 0.176, CI:0.011 0.266). Family health plays an indirect-only mediated role (the proportions of indirect-only mediated roles are 11.2%, 12.4%, and 11.5%) in the relationship between smartphone use and adolescent students' partial health risk behaviors: (CI: -0.042 -0.002), (CI: -0.049 -0.005), and (CI: -0.043 -0.002). These findings provided a theoretical and practical basis for better interventions in adolescent health risk behaviors.
Collapse
Affiliation(s)
- Fangmin Gong
- School of Literature and Journalism Communication, Jishou University, Jishou 416000, China
| | - Zhaowen Lei
- School of Literature and Journalism Communication, Jishou University, Jishou 416000, China
| | - Zhuliu Gong
- School of Literature and Journalism Communication, Jishou University, Jishou 416000, China
| | - Hewei Min
- School of Public Health, Peking University, Beijing 100871, China
| | - Pu Ge
- Bachelor of Pharmacy Institute of Chinese Medicinal Sciences, University of Macau, Macao 999078, China
| | - Yi Guo
- School of Public Health, Peking University, Beijing 100871, China
| | - Wai-Kit Ming
- Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong 999077, China
| | - Xinying Sun
- School of Public Health, Peking University, Beijing 100871, China
| | - Yibo Wu
- School of Public Health, Peking University, Beijing 100871, China
| |
Collapse
|
50
|
Deng T, Barman-Adhikari A, Lee YJ, Dewri R, Bender K. Substance use and sentiment and topical tendencies: a study using social media conversations of youth experiencing homelessness. INFORMATION TECHNOLOGY & PEOPLE 2022. [DOI: 10.1108/itp-12-2020-0860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThis study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.Design/methodology/approachParticipants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.FindingsFacebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.Originality/valueDigital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.
Collapse
|