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Zhang Y, Fu J, Lai J, Deng S, Guo Z, Zhong C, Tang J, Cao W, Wu Y. Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review. J Med Internet Res 2024; 26:e51496. [PMID: 38758590 PMCID: PMC11143395 DOI: 10.2196/51496] [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: 08/02/2023] [Revised: 11/29/2023] [Accepted: 04/16/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND The internet community has become a significant source for researchers to conduct qualitative studies analyzing users' views, attitudes, and experiences about public health. However, few studies have assessed the ethical issues in qualitative research using social media data. OBJECTIVE This study aims to review the reportage of ethical considerations in qualitative research utilizing social media data on public health care. METHODS We performed a scoping review of studies mining text from internet communities and published in peer-reviewed journals from 2010 to May 31, 2023. These studies, limited to the English language, were retrieved to evaluate the rates of reporting ethical approval, informed consent, and privacy issues. We searched 5 databases, that is, PubMed, Web of Science, CINAHL, Cochrane, and Embase. Gray literature was supplemented from Google Scholar and OpenGrey websites. Studies using qualitative methods mining text from the internet community focusing on health care topics were deemed eligible. Data extraction was performed using a standardized data extraction spreadsheet. Findings were reported using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. RESULTS After 4674 titles, abstracts, and full texts were screened, 108 studies on mining text from the internet community were included. Nearly half of the studies were published in the United States, with more studies from 2019 to 2022. Only 59.3% (64/108) of the studies sought ethical approval, 45.3% (49/108) mentioned informed consent, and only 12.9% (14/108) of the studies explicitly obtained informed consent. Approximately 86% (12/14) of the studies that reported informed consent obtained digital informed consent from participants/administrators, while 14% (2/14) did not describe the method used to obtain informed consent. Notably, 70.3% (76/108) of the studies contained users' written content or posts: 68% (52/76) contained verbatim quotes, while 32% (24/76) paraphrased the quotes to prevent traceability. However, 16% (4/24) of the studies that paraphrased the quotes did not report the paraphrasing methods. Moreover, 18.5% (20/108) of the studies used aggregated data analysis to protect users' privacy. Furthermore, the rates of reporting ethical approval were different between different countries (P=.02) and between papers that contained users' written content (both direct and paraphrased quotes) and papers that did not contain users' written content (P<.001). CONCLUSIONS Our scoping review demonstrates that the reporting of ethical considerations is widely neglected in qualitative research studies using social media data; such studies should be more cautious in citing user quotes to maintain user privacy. Further, our review reveals the need for detailed information on the precautions of obtaining informed consent and paraphrasing to reduce the potential bias. A national consensus of ethical considerations such as ethical approval, informed consent, and privacy issues is needed for qualitative research of health care using social media data of internet communities.
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Affiliation(s)
- Yujie Zhang
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Jiaqi Fu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Jie Lai
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Shisi Deng
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Zihan Guo
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Chuhan Zhong
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Jianyao Tang
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Wenqiong Cao
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yanni Wu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
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Chart-Pascual JP, Montero-Torres M, Ortega MA, Mar-Barrutia L, Zorrilla Martinez I, Alvarez-Mon M, Gonzalez-Pinto A, Alvarez-Mon MA. Areas of interest and sentiment analysis towards second generation antipsychotics, lithium and mood stabilizing anticonvulsants: Unsupervised analysis using Twitter. J Affect Disord 2024; 351:649-660. [PMID: 38290587 DOI: 10.1016/j.jad.2024.01.234] [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: 08/21/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/01/2024]
Abstract
BACKGROUND Severe mental disorders like Schizophrenia and related psychotic disorders (SRD) or Bipolar Disorder (BD) require pharmacological treatment for relapse prevention and quality of life improvement. Yet, treatment adherence is a challenge, partly due to patients' attitudes and beliefs towards their medication. Social media listening offers insights into patient experiences and preferences, particularly in severe mental disorders. METHODS All tweets posted between 2008 and 2022 mentioning the names of the main drugs used in SRD and BD were analyzed using advanced artificial intelligence techniques such as machine learning, and deep learning, along with natural language processing. RESULTS In this 15-year study analyzing 893,289 tweets, second generation antipsychotics received more mentions in English tweets, whereas mood stabilizers received more tweets in Spanish. English tweets about economic and legal aspects displayed negative emotions, while Spanish tweets seeking advice showed surprise. Moreover, a recurring theme in Spanish tweets was the shortage of medications, evoking feelings of anger among users. LIMITATIONS This study's analysis of Twitter data, while insightful, may not fully capture the nuances of discussions due to the platform's brevity. Additionally, the wide therapeutic use of the studied drugs, complicates the isolation of disorder-specific discourse. Only English and Spanish tweets were examined, limiting the cultural breadth of the findings. CONCLUSION This study emphasizes the importance of social media research in understanding user perceptions of SRD and BD treatments. The results provide valuable insights for clinicians when considering how patients and the general public view and communicate about these treatments in the digital environment.
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Affiliation(s)
- Juan Pablo Chart-Pascual
- Psychiatry Department, Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain; University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain; Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain; CIBERSAM.
| | - Maria Montero-Torres
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain; Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcala de Henares, Madrid, Spain
| | - Miguel Angel Ortega
- Cancer Registry and Pathology Department, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain; Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain; Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcala de Henares, Madrid, Spain
| | - Lorea Mar-Barrutia
- Psychiatry Department, Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain; Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain; CIBERSAM
| | - Iñaki Zorrilla Martinez
- Psychiatry Department, Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain; University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain; Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain; CIBERSAM
| | - Melchor Alvarez-Mon
- Immune System Diseases-Rheumatology and Internal Medicine Service, Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas, University Hospital Príncipe de Asturias, Alcala de Henares, Spain; Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain; Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcala de Henares, Madrid, Spain
| | - Ana Gonzalez-Pinto
- Psychiatry Department, Osakidetza Basque Health Service, Araba University Hospital, Vitoria-Gasteiz, Spain; University of the Basque Country UPV/EHU, Vitoria-Gasteiz, Spain; Bioaraba Health Research Institute, Vitoria-Gasteiz, Spain; CIBERSAM
| | - Miguel Angel Alvarez-Mon
- Ramón y Cajal Institute of Sanitary Research (IRYCIS), 28034 Madrid, Spain; Department of Medicine and Medical Specialities, University of Alcala, 28801 Alcala de Henares, Madrid, Spain; Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, Madrid, Spain
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Carabot F, Donat-Vargas C, Santoma-Vilaclara J, Ortega MA, García-Montero C, Fraile-Martínez O, Zaragoza C, Monserrat J, Alvarez-Mon M, Alvarez-Mon MA. Exploring Perceptions About Paracetamol, Tramadol, and Codeine on Twitter Using Machine Learning: Quantitative and Qualitative Observational Study. J Med Internet Res 2023; 25:e45660. [PMID: 37962927 PMCID: PMC10685273 DOI: 10.2196/45660] [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: 01/11/2023] [Revised: 08/18/2023] [Accepted: 08/23/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND Paracetamol, codeine, and tramadol are commonly used to manage mild pain, and their availability without prescription or medical consultation raises concerns about potential opioid addiction. OBJECTIVE This study aims to explore the perceptions and experiences of Twitter users concerning these drugs. METHODS We analyzed the tweets in English or Spanish mentioning paracetamol, tramadol, or codeine posted between January 2019 and December 2020. Out of 152,056 tweets collected, 49,462 were excluded. The content was categorized using a codebook, distinguishing user types (patients, health care professionals, and institutions), and classifying medical content based on efficacy and adverse effects. Scientific accuracy and nonmedical content themes (commercial, economic, solidarity, and trivialization) were also assessed. A total of 1000 tweets for each drug were manually classified to train, test, and validate machine learning classifiers. RESULTS Of classifiable tweets, 42,840 mentioned paracetamol and 42,131 mentioned weak opioids (tramadol or codeine). Patients accounted for 73.10% (60,771/83,129) of the tweets, while health care professionals and institutions received the highest like-tweet and tweet-retweet ratios. Medical content distribution significantly differed for each drug (P<.001). Nonmedical content dominated opioid tweets (23,871/32,307, 73.9%), while paracetamol tweets had a higher prevalence of medical content (33,943/50,822, 66.8%). Among medical content tweets, 80.8% (41,080/50,822) mentioned drug efficacy, with only 6.9% (3501/50,822) describing good or sufficient efficacy. Nonmedical content distribution also varied significantly among the different drugs (P<.001). CONCLUSIONS Patients seeking relief from pain are highly interested in the effectiveness of drugs rather than potential side effects. Alarming trends include a significant number of tweets trivializing drug use and recreational purposes, along with a lack of awareness regarding side effects. Monitoring conversations related to analgesics on social media is essential due to common illegal web-based sales and purchases without prescriptions.
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Affiliation(s)
- Federico Carabot
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Ramon y Cajal Institute of Sanitary Research, Madrid, Spain
| | - Carolina Donat-Vargas
- Institute of Environmental Medicine, Karolinska Institutet, Unit of Cardiovascular and Nutritional Epidemiology, Stockholm, Sweden
- ISGlobal, Institut de Salut Global de Barcelona, Campus MAR, Barcelona, Spain
- Centro de Investigación Biomédica en Red Epidemiología y Salud Pública, Madrid, Spain
| | - Javier Santoma-Vilaclara
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Data & AI, Filament Consultancy Group., London, United Kingdom
| | - Miguel A Ortega
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Ramon y Cajal Institute of Sanitary Research, Madrid, Spain
- Cancer Registry and Pathology Department, Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Spain
| | - Cielo García-Montero
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Ramon y Cajal Institute of Sanitary Research, Madrid, Spain
| | - Oscar Fraile-Martínez
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Ramon y Cajal Institute of Sanitary Research, Madrid, Spain
| | - Cristina Zaragoza
- Biomedical Sciences Department, University of Alcalá, Pharmacology Unit, Alcala de Henares, Spain
| | - Jorge Monserrat
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Ramon y Cajal Institute of Sanitary Research, Madrid, Spain
| | - Melchor Alvarez-Mon
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Ramon y Cajal Institute of Sanitary Research, Madrid, Spain
- Immune System Diseases-Rheumatology and Internal Medicine Service, Centro de Investigación Biomédica en Red Enfermedades Hepáticas y Digestivas, University Hospital Príncipe de Asturias, Alcala de Henares, Spain
| | - Miguel Angel Alvarez-Mon
- Department of Medicine and Medical Specialities, University of Alcalá, Alcalá de Henares, Spain
- Ramon y Cajal Institute of Sanitary Research, Madrid, Spain
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, Madrid, Spain
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Arillotta D, Floresta G, Guirguis A, Corkery JM, Catalani V, Martinotti G, Sensi SL, Schifano F. GLP-1 Receptor Agonists and Related Mental Health Issues; Insights from a Range of Social Media Platforms Using a Mixed-Methods Approach. Brain Sci 2023; 13:1503. [PMID: 38002464 PMCID: PMC10669484 DOI: 10.3390/brainsci13111503] [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: 09/28/2023] [Revised: 10/16/2023] [Accepted: 10/23/2023] [Indexed: 11/26/2023] Open
Abstract
The emergence of glucagon-like peptide-1 receptor agonists (GLP-1 RAs; semaglutide and others) now promises effective, non-invasive treatment of obesity for individuals with and without diabetes. Social media platforms' users started promoting semaglutide/Ozempic as a weight-loss treatment, and the associated increase in demand has contributed to an ongoing worldwide shortage of the drug associated with levels of non-prescribed semaglutide intake. Furthermore, recent reports emphasized some GLP-1 RA-associated risks of triggering depression and suicidal thoughts. Consistent with the above, we aimed to assess the possible impact of GLP-1 RAs on mental health as being perceived and discussed in popular open platforms with the help of a mixed-methods approach. Reddit posts yielded 12,136 comments, YouTube videos 14,515, and TikTok videos 17,059, respectively. Out of these posts/entries, most represented matches related to sleep-related issues, including insomnia (n = 620 matches); anxiety (n = 353); depression (n = 204); and mental health issues in general (n = 165). After the initiation of GLP-1 RAs, losing weight was associated with either a marked improvement or, in some cases, a deterioration, in mood; increase/decrease in anxiety/insomnia; and better control of a range of addictive behaviors. The challenges of accessing these medications were a hot topic as well. To the best of our knowledge, this is the first study documenting if and how GLP-1 RAs are perceived as affecting mood, mental health, and behaviors. Establishing a clear cause-and-effect link between metabolic diseases, depression and medications is difficult because of their possible reciprocal relationship, shared underlying mechanisms and individual differences. Further research is needed to better understand the safety profile of these molecules and their putative impact on behavioral and non-behavioral addictions.
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Affiliation(s)
- Davide Arillotta
- School of Clinical Pharmacology and Toxicology, University of Florence, 50121 Florence, Italy;
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK; (G.F.); (A.G.); (J.M.C.); (V.C.); (G.M.)
| | - Giuseppe Floresta
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK; (G.F.); (A.G.); (J.M.C.); (V.C.); (G.M.)
- Department of Drug and Health Sciences, University of Catania, 95124 Catania, Italy
| | - Amira Guirguis
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK; (G.F.); (A.G.); (J.M.C.); (V.C.); (G.M.)
- Pharmacy, Swansea University Medical School, Faculty of Medicine, Health and Life Science, Swansea University, Swansea SA2 8PP, UK
| | - John Martin Corkery
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK; (G.F.); (A.G.); (J.M.C.); (V.C.); (G.M.)
| | - Valeria Catalani
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK; (G.F.); (A.G.); (J.M.C.); (V.C.); (G.M.)
| | - Giovanni Martinotti
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK; (G.F.); (A.G.); (J.M.C.); (V.C.); (G.M.)
- Department of Neurosciences, Imaging and Clinical Sciences, University of Chieti-Pescara, 66100 Chieti, Italy;
| | - Stefano L. Sensi
- Department of Neurosciences, Imaging and Clinical Sciences, University of Chieti-Pescara, 66100 Chieti, Italy;
- Center for Advanced Studies and Technology (CAST), Institute of Advanced Biomedical Technology (ITAB), University of Chieti-Pescara, Via dei Vestini 21, 66100 Chieti, Italy
| | - Fabrizio Schifano
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield AL10 9AB, UK; (G.F.); (A.G.); (J.M.C.); (V.C.); (G.M.)
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Fu J, Li C, Zhou C, Li W, Lai J, Deng S, Zhang Y, Guo Z, Wu Y. Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review. J Med Internet Res 2023; 25:e43349. [PMID: 37358900 DOI: 10.2196/43349] [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: 10/10/2022] [Revised: 05/28/2023] [Accepted: 05/30/2023] [Indexed: 06/27/2023] Open
Abstract
BACKGROUND Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care. OBJECTIVE This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care? METHODS A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. RESULTS Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education). CONCLUSIONS Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.
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Affiliation(s)
- Jiaqi Fu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Chaixiu Li
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Chunlan Zhou
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenji Li
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jie Lai
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Shisi Deng
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Yujie Zhang
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Zihan Guo
- Nanfang Hospital, Southern Medical University, Guangzhou, China
- School of Nursing, Southern Medical University, Guangzhou, China
| | - Yanni Wu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
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Golder S, Stevens R, O'Connor K, James R, Gonzalez-Hernandez G. Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review. J Med Internet Res 2022; 24:e35788. [PMID: 35486433 PMCID: PMC9107046 DOI: 10.2196/35788] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/08/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.
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Affiliation(s)
- Su Golder
- Department of Health Sciences, University of York, York, United Kingdom
| | - Robin Stevens
- School of Communication and Journalism, University of Southern California, Los Angeles, CA, United States
| | - Karen O'Connor
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Richard James
- School of Nursing Liaison and Clinical Outreach Coordinator, University of Pennsylvania, Philadelphia, PA, United States
| | - Graciela Gonzalez-Hernandez
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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de Anta L, Alvarez-Mon MA, Ortega MA, Salazar C, Donat-Vargas C, Santoma-Vilaclara J, Martin-Martinez M, Lahera G, Gutierrez-Rojas L, Rodriguez-Jimenez R, Quintero J, Alvarez-Mon M. Areas of Interest and Social Consideration of Antidepressants on English Tweets: A Natural Language Processing Classification Study. J Pers Med 2022; 12:jpm12020155. [PMID: 35207644 PMCID: PMC8879287 DOI: 10.3390/jpm12020155] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/11/2022] [Accepted: 01/18/2022] [Indexed: 01/11/2023] Open
Abstract
Background: Antidepressants are the foundation of the treatment of major depressive disorders. Despite the scientific evidence, there is still a sustained debate and concern about the efficacy of antidepressants, with widely differing opinions among the population about their positive and negative effects, which may condition people’s attitudes towards such treatments. Our aim is to investigate Twitter posts about antidepressants in order to have a better understanding of the social consideration of antidepressants. Methods: We gathered public tweets mentioning antidepressants written in English, published throughout a 22-month period, between 1 January 2019 and 31 October 2020. We analysed the content of each tweet, determining in the first place whether they included medical aspects or not. Those with medical content were classified into four categories: general aspects, such as quality of life or mood, sleep-related conditions, appetite/weight issues and aspects around somatic alterations. In non-medical tweets, we distinguished three categories: commercial nature (including all economic activity, drug promotion, education or outreach), help request/offer, and drug trivialization. In addition, users were arranged into three categories according to their nature: patients and relatives, caregivers, and interactions between Twitter users. Finally, we identified the most mentioned antidepressants, including the number of retweets and likes, which allowed us to measure the impact among Twitter users. Results: The activity in Twitter concerning antidepressants is mainly focused on the effects these drugs may have on certain health-related areas, specifically sleep (20.87%) and appetite/weight (8.95%). Patients and relatives are the type of user that most frequently posts tweets with medical content (65.2%, specifically 80% when referencing sleep and 78.6% in the case of appetite/weight), whereas they are responsible for only 2.9% of tweets with non-medical content. Among tweets classified as non-medical in this study, the most common subject was drug trivialization (66.86%). Caregivers barely have any presence in conversations in Twitter about antidepressants (3.5%). However, their tweets rose more interest among other users, with a ratio 11.93 times higher than those posted by patients and their friends and family. Mirtazapine is the most mentioned antidepressant in Twitter (45.43%), with a significant difference with the rest, agomelatine (11.11%). Conclusions: This study shows that Twitter users that take antidepressants, or their friends and family, use social media to share medical information about antidepressants. However, other users that do not talk about antidepressants from a personal or close experience, frequently do so in a stigmatizing manner, by trivializing them. Our study also brings to light the scarce presence of caregivers in Twitter.
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Affiliation(s)
- Laura de Anta
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain; (L.d.A.); (M.M.-M.); (J.Q.)
| | - Miguel Angel Alvarez-Mon
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain; (L.d.A.); (M.M.-M.); (J.Q.)
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain; (G.L.); (M.A.-M.)
- Correspondence: (M.A.A.-M.); (M.A.O.)
| | - Miguel A. Ortega
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain; (G.L.); (M.A.-M.)
- Ramón y Cajal Health Research Institute (IRYCIS), 28034 Madrid, Spain
- Correspondence: (M.A.A.-M.); (M.A.O.)
| | - Cristina Salazar
- Departamento Teoría de la Señal y Comunicaciones y Sistemas Telemáticos y Computación, Escuela Técnica Superior de Ingeniería de Telecomunicación, Universidad Rey Juan Carlos, 28942 Fuenlabrada, Spain;
| | - Carolina Donat-Vargas
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine (IMM), Karolinska Institute, 171 77 Stockholm, Sweden;
| | | | - Maria Martin-Martinez
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain; (L.d.A.); (M.M.-M.); (J.Q.)
| | - Guillermo Lahera
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain; (G.L.); (M.A.-M.)
- Ramón y Cajal Health Research Institute (IRYCIS), 28034 Madrid, Spain
- CIBERSAM (Biomedical Research Networking Centre in Mental Health), 22807 Madrid, Spain;
- Psychiatry Service, Príncipe de Asturias University Hospital, 28805 Alcalá de Henares, Spain
| | | | - Roberto Rodriguez-Jimenez
- CIBERSAM (Biomedical Research Networking Centre in Mental Health), 22807 Madrid, Spain;
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas 12), Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain
| | - Javier Quintero
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain; (L.d.A.); (M.M.-M.); (J.Q.)
| | - Melchor Alvarez-Mon
- Department of Medicine and Medical Specialities, Faculty of Medicine and Health Sciences, University of Alcalá, 28801 Alcalá de Henares, Spain; (G.L.); (M.A.-M.)
- Ramón y Cajal Health Research Institute (IRYCIS), 28034 Madrid, Spain
- Immune System Diseases-Rheumatology and Oncology Service, University Hospital Príncipe de Asturias, CIBEREHD, 28805 Alcalá de Henares, Spain
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Heyerdahl LW, Lana B, Giles-Vernick T. The Impact of the Online COVID-19 Infodemic on French Red Cross Actors' Field Engagement and Protective Behaviors: Mixed Methods Study. JMIR INFODEMIOLOGY 2021; 1:e27472. [PMID: 34661065 PMCID: PMC8507423 DOI: 10.2196/27472] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 04/27/2021] [Accepted: 07/16/2021] [Indexed: 01/20/2023]
Abstract
Background The COVID-19 pandemic has been widely described as an infodemic, an excess of rapidly circulating information in social and traditional media in which some information may be erroneous, contradictory, or inaccurate. One key theme cutting across many infodemic analyses is that it stymies users’ capacities to identify appropriate information and guidelines, encourages them to take inappropriate or even harmful actions, and should be managed through multiple transdisciplinary approaches. Yet, investigations demonstrating how the COVID-19 information ecosystem influences complex public decision making and behavior offline are relatively few. Objective The aim of this study was to investigate whether information reported through the social media channel Twitter, linked articles and websites, and selected traditional media affected the risk perception, engagement in field activities, and protective behaviors of French Red Cross (FRC) volunteers and health workers in the Paris region of France from June to October 2020. Methods We used a hybrid approach that blended online and offline data. We tracked daily Twitter discussions and selected traditional media in France for 7 months, qualitatively evaluating COVID-19 claims and debates about nonpharmaceutical protective measures. We conducted 24 semistructured interviews with FRC workers and volunteers. Results Social and traditional media debates about viral risks and nonpharmaceutical interventions fanned anxieties among FRC volunteers and workers. Decisions to continue conducting FRC field activities and daily protective practices were also influenced by other factors unrelated to the infodemic: familial and social obligations, gender expectations, financial pressures, FRC rules and communications, state regulations, and relationships with coworkers. Some respondents developed strategies for “tuning out” social and traditional media. Conclusions This study suggests that during the COVID-19 pandemic, the information ecosystem may be just one among multiple influences on one group’s offline perceptions and behavior. Measures to address users who have disengaged from online sources of health information and who rely on social relationships to obtain information are needed. Tuning out can potentially lead to less informed decision making, leading to worse health outcomes.
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Affiliation(s)
- Leonardo W Heyerdahl
- Department of Global Health, Anthropology and Ecology of Disease Emergence Unit Institut Pasteur Paris France
| | - Benedetta Lana
- Department of Global Health, Anthropology and Ecology of Disease Emergence Unit Institut Pasteur Paris France
| | - Tamara Giles-Vernick
- Department of Global Health, Anthropology and Ecology of Disease Emergence Unit Institut Pasteur Paris France
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9
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Areas of Interest and Attitudes towards the Pharmacological Treatment of Attention Deficit Hyperactivity Disorder: Thematic and Quantitative Analysis Using Twitter. J Clin Med 2021; 10:jcm10122668. [PMID: 34204353 PMCID: PMC8235344 DOI: 10.3390/jcm10122668] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 06/08/2021] [Accepted: 06/13/2021] [Indexed: 01/23/2023] Open
Abstract
We focused on tweets containing hashtags related to ADHD pharmacotherapy between 20 September and 31 October 2019. Tweets were classified as to whether they described medical issues or not. Tweets with medical content were classified according to the topic they referred to: side effects, efficacy, or adherence. Furthermore, we classified any links included within a tweet as either scientific or non-scientific. We created a dataset of 6568 tweets: 4949 (75.4%) related to stimulants, 605 (9.2%) to non-stimulants and 1014 (15.4%) to alpha-2 agonists. Next, we manually analyzed 1810 tweets. In the end, 481 (48%) of the tweets in the stimulant group, 218 (71.9%) in the non-stimulant group and 162 (31.9%) in the alpha agonist group were considered classifiable. Stimulants accumulated the majority of tweets. Notably, the content that generated the highest frequency of tweets was that related to treatment efficacy, with alpha-2 agonist-related tweets accumulating the highest proportion of positive consideration. We found the highest percentages of tweets with scientific links in those posts related to alpha-2 agonists. Stimulant-related tweets obtained the highest proportion of likes and were the most disseminated within the Twitter community. Understanding the public view of these medications is necessary to design promotional strategies aimed at the appropriate population.
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10
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Alvarez-Mon MA, Donat-Vargas C, Santoma-Vilaclara J, de Anta L, Goena J, Sanchez-Bayona R, Mora F, Ortega MA, Lahera G, Rodriguez-Jimenez R, Quintero J, Álvarez-Mon M. Assessment of Antipsychotic Medications on Social Media: Machine Learning Study. Front Psychiatry 2021; 12:737684. [PMID: 34867531 PMCID: PMC8637121 DOI: 10.3389/fpsyt.2021.737684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 10/19/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Antipsychotic medications are the first-line treatment for schizophrenia. However, non-adherence is frequent despite its negative impact on the course of the illness. In response, we aimed to investigate social media posts about antipsychotics to better understand the online environment in this regard. Methods: We collected tweets containing mentions of antipsychotic medications posted between January 1st 2019 and October 31st 2020. The content of each tweet and the characteristics of the users were analyzed as well as the number of retweets and likes generated. Results: Twitter users, especially those identified as patients, showed an interest in antipsychotic medications, mainly focusing on the topics of sexual dysfunction and sedation. Interestingly, paliperidone, despite being among one of the newest antipsychotics, accounted for a low number of tweets and did not generate much interest. Conversely, retweet and like ratios were higher in those tweets asking for or offering help, in those posted by institutions and in those mentioning cognitive complaints. Moreover, health professionals did not have a strong presence in tweet postings, nor did medical institutions. Finally, trivialization was frequently observed. Conclusion: This analysis of tweets about antipsychotic medications provides insights into experiences and opinions related to this treatment. Twitter user perspectives therefore constitute a valuable input that may help to improve clinicians' knowledge of antipsychotic medications and their communication with patients regarding this treatment.
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Affiliation(s)
- Miguel A Alvarez-Mon
- Department of Medicine and Medical Specialities, University of Alcala, Alcala de Henares, Spain.,Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain.,Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, Madrid, Spain
| | - Carolina Donat-Vargas
- Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden.,IMDEA-Food Institute, CEI UAM+CSIC, Madrid, Spain
| | | | - Laura de Anta
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, Madrid, Spain
| | - Javier Goena
- Department of Psychiatry and Clinical Psychology, University of Navarra Clinic, Pamplona, Spain.,Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Rodrigo Sanchez-Bayona
- Hospital Universitario 12 de Octubre, Unidad de Cáncer de Mama y Ginecológico, Madrid, Spain
| | - Fernando Mora
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, Madrid, Spain.,Department of Legal and Psychiatry, Complutense University, Madrid, Spain
| | - Miguel A Ortega
- Department of Medicine and Medical Specialities, University of Alcala, Alcala de Henares, Spain.,Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain
| | - Guillermo Lahera
- Department of Medicine and Medical Specialities, University of Alcala, Alcala de Henares, Spain.,Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain.,Department of Psychiatry, University Hospital Principe de Asturias, Alcalá de Henares, Spain.,CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain
| | - Roberto Rodriguez-Jimenez
- CIBERSAM (Biomedical Research Networking Centre in Mental Health), Madrid, Spain.,Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas 12), Madrid, Spain.,Universidad Complutense de Madrid (UCM), Madrid, Spain
| | - Javier Quintero
- Department of Psychiatry and Mental Health, Hospital Universitario Infanta Leonor, Madrid, Spain.,Department of Legal and Psychiatry, Complutense University, Madrid, Spain
| | - Melchor Álvarez-Mon
- Department of Medicine and Medical Specialities, University of Alcala, Alcala de Henares, Spain.,Ramón y Cajal Institute of Sanitary Research (IRYCIS), Madrid, Spain.,Service of Internal Medicine and Immune System Diseases-Rheumatology, University Hospital Príncipe de Asturias (CIBEREHD), Alcalá de Henares, Spain
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