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Singh T, Roberts K, Cohen T, Cobb N, Franklin A, Myneni S. Discerning conversational context in online health communities for personalized digital behavior change solutions using Pragmatics to Reveal Intent in Social Media (PRISM) framework. J Biomed Inform 2023; 140:104324. [PMID: 36842490 PMCID: PMC10206862 DOI: 10.1016/j.jbi.2023.104324] [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: 06/05/2022] [Revised: 02/18/2023] [Accepted: 02/21/2023] [Indexed: 02/28/2023]
Abstract
BACKGROUND Online health communities (OHCs) have emerged as prominent platforms for behavior modification, and the digitization of online peer interactions has afforded researchers with unique opportunities to model multilevel mechanisms that drive behavior change. Existing studies, however, have been limited by a lack of methods that allow the capture of conversational context and socio-behavioral dynamics at scale, as manifested in these digital platforms. OBJECTIVE We develop, evaluate, and apply a novel methodological framework, Pragmatics to Reveal Intent in Social Media (PRISM), to facilitate granular characterization of peer interactions by combining multidimensional facets of human communication. METHODS We developed and applied PRISM to analyze peer interactions (N = 2.23 million) in QuitNet, an OHC for tobacco cessation. First, we generated a labeled set of peer interactions (n = 2,005) through manual annotation along three dimensions: communication themes (CTs), behavior change techniques (BCTs), and speech acts (SAs). Second, we used deep learning models to apply our qualitative codes at scale. Third, we applied our validated model to perform a retrospective analysis. Finally, using social network analysis (SNA), we portrayed large-scale patterns and relationships among the aforementioned communication dimensions embedded in peer interactions in QuitNet. RESULTS Qualitative analysis showed that the themes of social support and behavioral progress were common. The most used BCTs were feedback and monitoring and comparison of behavior, and users most commonly expressed their intentions using SAs-expressive and emotion. With additional in-domain pre-training, bidirectional encoder representations from Transformers (BERT) outperformed other deep learning models on the classification tasks. Content-specific SNA revealed that users' engagement or abstinence status is associated with the prevalence of various categories of BCTs and SAs, which also was evident from the visualization of network structures. CONCLUSIONS Our study describes the interplay of multilevel characteristics of online communication and their association with individual health behaviors.
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Affiliation(s)
- Tavleen Singh
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA.
| | - Kirk Roberts
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Trevor Cohen
- Biomedical Informatics and Medical Education, The University of Washington, Seattle, WA, USA
| | - Nathan Cobb
- Georgetown University Medical Center, Washington, DC, USA
| | - Amy Franklin
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Sahiti Myneni
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
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Rutherford BN, Lim CCW, Johnson B, Cheng B, Chung J, Huang S, Sun T, Leung J, Stjepanović D, Chan GCK. #TurntTrending: a systematic review of substance use portrayals on social media platforms. Addiction 2023; 118:206-217. [PMID: 36075258 PMCID: PMC10087142 DOI: 10.1111/add.16020] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 07/21/2022] [Indexed: 01/05/2023]
Abstract
AIMS There is a growing body of literature exploring the types of substance-related content and their portrayals on various social media platforms. We aimed to summarize how content related to substances is portrayed on various social media platforms. METHODS This systematic review was pre-registered on PROSPERO (ref: CRD42021291853). A comprehensive search was conducted in the databases of PubMed, Scopus, PsycINFO and Web of Science in April 2021. Original qualitative studies published post-2004 that included thematic and sentiment analyses of social media content on tobacco, alcohol, psychostimulant, e-cigarette, cannabis, opiate, stimulant/amphetamine, inhalant and novel psychoactive substance were included. Social media platforms were defined as online web- or application-based platforms that allowed users to generate content and interact via 'liking', comment or messaging features. Only studies that included summative and/or thematic content analyses of substance-related social media content were included. RESULTS A total of 73 studies, which covered 15 905 182 substance-related posts on Twitter, YouTube, Instagram, Pinterest, TikTok and Weibo, were identified. A total of 76.3% of all substance-related content was positive in its depiction of substance use, with 20.2% of content depicting use negatively. Sentiment regarding opiate use however was commonly negative (55.5%). Most studies identified themes relating to Health, Safety and Harms (65.0%) of substance use. Themes relating to Promotions/Advertisements (63.3%), Informative content (55.0%) and Use behaviours (43.3%) were also frequently identified. CONCLUSIONS Substance-related content that promotes engagement with substance use or actively depicts use appears to be widely available on social media. The large public presence of this content may have concerning influences on attitudes, behaviours and risk perceptions relating to substance use, particularly among the most vulnerable and heaviest users of social media-adolescents and young adults.
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Affiliation(s)
- Brienna N Rutherford
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.,School of Psychology, The University of Queensland, St Lucia, Australia
| | - Carmen C W Lim
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.,School of Psychology, The University of Queensland, St Lucia, Australia
| | - Benjamin Johnson
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.,School of Psychology, The University of Queensland, St Lucia, Australia
| | - Brandon Cheng
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.,School of Psychology, The University of Queensland, St Lucia, Australia
| | - Jack Chung
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.,School of Psychology, The University of Queensland, St Lucia, Australia
| | - Sandy Huang
- School of Medicine, The University of Queensland, St Lucia, Australia
| | - Tianze Sun
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia.,School of Psychology, The University of Queensland, St Lucia, Australia
| | - Janni Leung
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia
| | - Daniel Stjepanović
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia
| | - Gary C K Chan
- National Centre for Youth Substance Use Research, The University of Queensland, St Lucia, Australia
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Lee J, Suttiratana SC, Sen I, Kong G. E-Cigarette Marketing on Social Media: A Scoping Review. CURRENT ADDICTION REPORTS 2023. [DOI: 10.1007/s40429-022-00463-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Fu R, Kundu A, Mitsakakis N, Elton-Marshall T, Wang W, Hill S, Bondy SJ, Hamilton H, Selby P, Schwartz R, Chaiton MO. Machine learning applications in tobacco research: a scoping review. Tob Control 2023; 32:99-109. [PMID: 34452986 DOI: 10.1136/tobaccocontrol-2020-056438] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Accepted: 04/14/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Identify and review the body of tobacco research literature that self-identified as using machine learning (ML) in the analysis. DATA SOURCES MEDLINE, EMABSE, PubMed, CINAHL Plus, APA PsycINFO and IEEE Xplore databases were searched up to September 2020. Studies were restricted to peer-reviewed, English-language journal articles, dissertations and conference papers comprising an empirical analysis where ML was identified to be the method used to examine human experience of tobacco. Studies of genomics and diagnostic imaging were excluded. STUDY SELECTION Two reviewers independently screened the titles and abstracts. The reference list of articles was also searched. In an iterative process, eligible studies were classified into domains based on their objectives and types of data used in the analysis. DATA EXTRACTION Using data charting forms, two reviewers independently extracted data from all studies. A narrative synthesis method was used to describe findings from each domain such as study design, objective, ML classes/algorithms, knowledge users and the presence of a data sharing statement. Trends of publication were visually depicted. DATA SYNTHESIS 74 studies were grouped into four domains: ML-powered technology to assist smoking cessation (n=22); content analysis of tobacco on social media (n=32); smoker status classification from narrative clinical texts (n=6) and tobacco-related outcome prediction using administrative, survey or clinical trial data (n=14). Implications of these studies and future directions for ML researchers in tobacco control were discussed. CONCLUSIONS ML represents a powerful tool that could advance the research and policy decision-making of tobacco control. Further opportunities should be explored.
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Affiliation(s)
- Rui Fu
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Anasua Kundu
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Nicholas Mitsakakis
- Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
- Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada
| | - Tara Elton-Marshall
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Sean Hill
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Susan J Bondy
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Hayley Hamilton
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Peter Selby
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Robert Schwartz
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Michael Oliver Chaiton
- Ontario Tobacco Research Unit, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Mental Health Policy Research, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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Wang Y, Duan Z, Weaver SR, Popova L, Spears CA, Ashley DL, Pechacek TF, Eriksen MP, Huang J. Consumption of JUUL vs. Other E-Cigarette Brands among U.S. E-Cigarette Users: Evidence from Wave 5 of the PATH Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:10837. [PMID: 36078551 PMCID: PMC9518567 DOI: 10.3390/ijerph191710837] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/26/2022] [Accepted: 08/28/2022] [Indexed: 06/15/2023]
Abstract
This study examines the use of JUUL vs. other e-cigarette brands among U.S. youth (12-17 years), young adult (18-24 years), and adult (25 years and above) e-cigarette users. Data were from the Population Assessment of Tobacco and Health (PATH) study Wave 5 survey (2019). The study population was past 30-day e-cigarette users who knew the brand of e-cigarettes they usually/last used (N = 2569). JUUL use was reported by 65.2% of youth, 60.7% of young adult, and 25.6% of adult e-cigarette users in our study sample. The share of JUUL consumed in the past 30 days, measured by the total number of puffs, was 15.4% by youth, 55.5% by young adults, and 29.1% by adults. By contrast, the share of other e-cigarettes consumed was 4.2% by youth, 28.9% by young adults, and 66.9% by adults. Youth JUUL users were more likely to use e-cigarettes within 30 min after waking (aOR = 2.30, 95% CI: 1.12-4.75) than youth users of other brands of e-cigarettes. Additionally, youth e-cigarette users who currently smoked cigarettes were less likely to use JUUL (aOR = 0.55, 95% CI: 0.30-0.99). This study concludes that JUUL consumption was disproportionally higher among youth and young adults in the U.S. in 2019.
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Affiliation(s)
- Yu Wang
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
| | - Zongshuan Duan
- Milken Institute School of Public Health, George Washington University, Washington, DC 20052, USA
| | - Scott R. Weaver
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
| | - Lucy Popova
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
| | - Claire A. Spears
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
| | - David L. Ashley
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
| | - Terry F. Pechacek
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
| | - Michael P. Eriksen
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
| | - Jidong Huang
- School of Public Health, Georgia State University, Atlanta, GA 30302, USA
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Purushothaman V, McMann TJ, Li Z, Cuomo RE, Mackey TK. Content and trend analysis of user-generated nicotine
sickness tweets: A retrospective infoveillance study. Tob Induc Dis 2022; 20:30. [PMID: 35529325 PMCID: PMC8919180 DOI: 10.18332/tid/145941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 12/02/2022] Open
Abstract
INTRODUCTION Exposure to pro-tobacco and electronic nicotine delivery system (ENDS) social media content can lead to overconsumption, increasing the likelihood of nicotine poisoning. This study aims to examine trends and characteristics of nicotine sickness content on Twitter between 2018–2020. METHODS Tweets were collected retrospectively from the Twitter Academic Research Application Programming Interface (API) stream filtered for keywords: ‘nic sick’, ‘nicsick’, ‘vape sick’, ‘vapesick’ between 2018–2020. Collected tweets were manually annotated to identify suspected user-generated reports of nicotine sickness and related themes using an inductive coding approach. The Augmented Dickey-Fuller (ADF) test was used to assess stationarity in the monthly variation of the volume of tweets between 2018–2020. RESULTS A total of 5651 tweets contained nicotine sickness-related keywords and 18.29% (n=1034) tweets reported one or more suspected nicotine sickness symptoms of varied severity. These tweets were also grouped into five related categories including firsthand and secondhand reports of symptoms, intentional overconsumption of nicotine products, users expressing intention to quit after ‘nic sick’ symptoms, mention of nicotine product type/brand name that they consumed while ‘nic sick’, and users discussing symptoms associated with nicotine withdrawal following cessation attempts. The volume of tweets reporting suspected nicotine sickness appeared to increase throughout the study period, except between February and April 2020. Stationarity in the volume of ‘nicsick’ tweets between 2018–2020 was not statistically significant (ADF= -0.32, p=0.98) indicating a change in the volume of tweets. CONCLUSIONS Results point to the need for alternative forms of adverse event surveillance and reporting, to appropriately capture the growing health burden of vaping. Infoveillance approaches on social media platforms can help to assess the volume and characteristics of user-generated content discussing suspected nicotine poisoning, which may not be reported to poison control centers. Increasing volume of user-reported nicotine sickness and intentional overconsumption of nicotine in twitter posts represent a concerning trend associated with ENDS-related adverse events and poisoning.
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Affiliation(s)
- Vidya Purushothaman
- Global Health Policy and Data Institute, San Diego, United States
- Division of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diego, San Diego, United States
| | - Tiana J. McMann
- Global Health Policy and Data Institute, San Diego, United States
- Global Health Program, Department of Anthropology, University of California, San Diego, San Diego, United States
| | - Zhuoran Li
- Global Health Policy and Data Institute, San Diego, United States
- Global Health Program, Department of Anthropology, University of California, San Diego, San Diego, United States
- S-3 Research, San Diego, United States
| | - Raphael E. Cuomo
- Global Health Policy and Data Institute, San Diego, United States
- Division of Infectious Diseases and Global Public Health, School of Medicine, University of California, San Diego, San Diego, United States
| | - Tim K. Mackey
- Global Health Policy and Data Institute, San Diego, United States
- Global Health Program, Department of Anthropology, University of California, San Diego, San Diego, United States
- S-3 Research, San Diego, United States
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Ren Y, Wu D, Singh A, Kasson E, Huang M, Cavazos-Rehg P. Automated Detection of Vaping-Related Tweets on Twitter During the 2019 EVALI Outbreak Using Machine Learning Classification. Front Big Data 2022; 5:770585. [PMID: 35224484 PMCID: PMC8866955 DOI: 10.3389/fdata.2022.770585] [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: 09/04/2021] [Accepted: 01/13/2022] [Indexed: 11/15/2022] Open
Abstract
There are increasingly strict regulations surrounding the purchase and use of combustible tobacco products (i.e., cigarettes); simultaneously, the use of other tobacco products, including e-cigarettes (i.e., vaping products), has dramatically increased. However, public attitudes toward vaping vary widely, and the health effects of vaping are still largely unknown. As a popular social media, Twitter contains rich information shared by users about their behaviors and experiences, including opinions on vaping. It is very challenging to identify vaping-related tweets to source useful information manually. In the current study, we proposed to develop a detection model to accurately identify vaping-related tweets using machine learning and deep learning methods. Specifically, we applied seven popular machine learning and deep learning algorithms, including Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, Multilayer Perception, Transformer Neural Network, and stacking and voting ensemble models to build our customized classification model. We extracted a set of sample tweets during an outbreak of e-cigarette or vaping-related lung injury (EVALI) in 2019 and created an annotated corpus to train and evaluate these models. After comparing the performance of each model, we found that the stacking ensemble learning achieved the highest performance with an F1-score of 0.97. All models could achieve 0.90 or higher after tuning hyperparameters. The ensemble learning model has the best average performance. Our study findings provide informative guidelines and practical implications for the automated detection of themed social media data for public opinions and health surveillance purposes.
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Affiliation(s)
- Yang Ren
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States
| | - Dezhi Wu
- Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States
| | - Avineet Singh
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, United States
| | - Erin Kasson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Ming Huang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States
| | - Patricia Cavazos-Rehg
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
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Benson R, Hu M, Chen AT, Zhu SH, Conway M. Examining Cannabis, Tobacco, and Vaping Discourse on Reddit: An Exploratory Approach Using Natural Language Processing. Front Public Health 2022; 9:738513. [PMID: 35071153 PMCID: PMC8766503 DOI: 10.3389/fpubh.2021.738513] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 11/26/2021] [Indexed: 12/01/2022] Open
Abstract
Background: Perceptions of tobacco, cannabis, and electronic nicotine delivery systems (ENDS) are continually evolving in the United States. Exploring these characteristics through user generated text sources may provide novel insights into product use behavior that are challenging to identify using survey-based methods. The objective of this study was to compare the topics frequently discussed among Reddit members in cannabis, tobacco, and ENDS-specific subreddits. Methods: We collected 643,070 posts on the social media site Reddit between January 2013 and December 2018. We developed and validated an annotation scheme, achieving a high level of agreement among annotators. We then manually coded a subset of 2,630 posts for their content with relation to experiences and use of the three products of interest, and further developed word cloud representations of the words contained in these posts. Finally, we applied Latent Dirichlet Allocation (LDA) topic modeling to the 643,070 posts to identify emerging themes related to cannabis, tobacco, and ENDS products being discussed on Reddit. Results: Our manual annotation process yielded 2,148 (81.6%) posts that contained a mention(s) of either cannabis, tobacco, or ENDS with 1,537 (71.5%) of these posts mentioning cannabis, 421 (19.5%) mentioning ENDS, and 264 (12.2%) mentioning tobacco. In cannabis-specific subreddits, personal experiences with cannabis, cannabis legislation, health effects of cannabis use, methods and forms of cannabis, and the cultivation of cannabis were commonly discussed topics. The discussion in tobacco-specific subreddits often focused on the discussion of brands and types of combustible tobacco, as well as smoking cessation experiences and advice. In ENDS-specific subreddits, topics often included ENDS accessories and parts, flavors and nicotine solutions, procurement of ENDS, and the use of ENDS for smoking cessation. Conclusion: Our findings highlight the posting and participation patterns of Reddit members in cannabis, tobacco, and ENDS-specific subreddits and provide novel insights into aspects of personal use regarding these products. These findings complement epidemiologic study designs and highlight the potential of using specific subreddits to explore personal experiences with cannabis, ENDS, and tobacco products.
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Affiliation(s)
- Ryzen Benson
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Mengke Hu
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Annie T. Chen
- Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, WA, United States
| | - Shu-Hong Zhu
- Moores Cancer Center, University of California, San Diego, La Jolla, CA, United States
| | - Mike Conway
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
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Singh T, Olivares S, Cohen T, Cobb N, Wang J, Franklin A, Myneni S. Pragmatics to Reveal Intent in Social Media Peer Interactions: Mixed Methods Study. J Med Internet Res 2021; 23:e32167. [PMID: 34787578 PMCID: PMC8663565 DOI: 10.2196/32167] [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: 07/16/2021] [Revised: 10/04/2021] [Accepted: 10/07/2021] [Indexed: 11/13/2022] Open
Abstract
Background Online health communities (OHCs) have emerged as the leading venues for behavior change and health-related information seeking. The soul and success of these digital platforms lie in their ability to foster social togetherness and a sense of community by providing personalized support. However, we have a minimal understanding of how conversational posts in these settings lead to collaborative societies and ultimately result in positive health changes through social influence. Objective Our objective is to develop a content-specific and intent-sensitive methodological framework for analyzing peer interactions in OHCs. Methods We developed and applied a mixed-methods approach to understand the manifestation of expressions in peer interactions in OHCs. We applied our approach to describe online social dialogue in the context of two online communities, QuitNet (QN) and the American Diabetes Association (ADA) support community. A total of 3011 randomly selected peer interactions (n=2005 from QN, n=1006 from ADA) were analyzed. Specifically, we conducted thematic analysis to characterize communication content and linguistic expressions (speech acts) embedded within the two data sets. We also developed an empirical user persona based on their engagement levels and behavior profiles. Further, we examined the association between speech acts and communication themes across observed tiers of user engagement and self-reported behavior profiles using the chi-square test or the Fisher test. Results Although social support, the most prevalent communication theme in both communities, was expressed in several subtle manners, the prevalence of emotions was higher in the tobacco cessation community and assertions were higher in the diabetes self-management (DSM) community. Specific communication theme-speech act relationships were revealed, such as the social support theme was significantly associated (P<.05) with 9 speech acts from a total of 10 speech acts (ie, assertion, commissive, declarative, desire, directive, expressive, question, stance, and statement) within the QN community. Only four speech acts (ie, commissive, emotion, expressive, and stance) were significantly associated (P<.05) with the social support theme in the ADA community. The speech acts were also significantly associated with the users’ abstinence status within the QN community and with the users’ lifestyle status within the ADA community (P<.05). Conclusions Such an overlay of communication intent implicit in online peer interactions alongside content-specific theory-linked characterizations of social media discourse can inform the development of effective digital health technologies in the field of health promotion and behavior change. Our analysis revealed a rich gradient of expressions across a standardized thematic vocabulary, with a distinct variation in emotional and informational needs, depending on the behavioral and disease management profiles within and across the communities. This signifies the need and opportunities for coupling pragmatic messaging in digital therapeutics and care management pathways for personalized support.
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Affiliation(s)
- Tavleen Singh
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
| | - Sofia Olivares
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
| | - Trevor Cohen
- Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Nathan Cobb
- Georgetown University Medical Center, Washington, DC, United States
| | - Jing Wang
- Florida State University College of Nursing, Tallahassee, FL, United States
| | - Amy Franklin
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
| | - Sahiti Myneni
- School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, United States
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Hu M, Benson R, Chen AT, Zhu SH, Conway M. Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing. Drug Alcohol Depend 2021; 228:109016. [PMID: 34560332 PMCID: PMC8801036 DOI: 10.1016/j.drugalcdep.2021.109016] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 07/17/2021] [Accepted: 07/23/2021] [Indexed: 01/10/2023]
Abstract
INTRODUCTION The relationship between cannabis, tobacco, and vaping devices is both rapidly changing and poorly understood, with consumers rapidly shifting between use of all three product types. Given this dynamic and evolving landscape, there is an urgent need to monitor and better understand co-use, dual-use, and transition patterns between these products. This study describes work that utilizes social media - in this case, Reddit - in conjunction with automated Natural Language Processing (NLP) methods to better understand cannabis, tobacco, and vaping device product usage patterns. METHODS We collected Reddit data from the period 2013-2018, sourced from eight popular, high-volume Reddit communities (subreddits) related to the three product categories. We then manually annotated (coded) a set of 2640 Reddit posts and trained a machine learning-based NLP algorithm to automatically identify and disambiguate between cannabis or tobacco mentions (both smoking and vaping) in Reddit posts. This classifier was then applied to all data derived from the eight subreddits, 767,788 posts in total. RESULTS The NLP algorithm achieved an overall moderate performance (overall F-score of 0.77). When applied to our large corpus of Reddit posts, we discovered that over 10% of posts in the smoking cessation subreddit r/stopsmoking were classified as referring to vaping nicotine, and that only 2% of posts from the subreddits r/electronic_cigarette and r/vaping were classified as referring to smoking (tobacco) cessation. CONCLUSIONS This study presents the results of applying an NLP algorithm designed to identify and distinguish between cannabis and tobacco mentions (both smoking and vaping) in Reddit posts, hence contributing to our currently limited understanding of co-use, dual-use, and transition patterns between these products.
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Affiliation(s)
- Mengke Hu
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
| | - Ryzen Benson
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
| | - Annie T Chen
- Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, WA, United States
| | - Shu-Hong Zhu
- Herbert Wertheim School of Public Health, University of California San Diego, La Jolla, CA, United States
| | - Mike Conway
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
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11
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Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health Surveill 2020; 6:e21660. [PMID: 33252345 PMCID: PMC7735906 DOI: 10.2196/21660] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 10/05/2020] [Accepted: 11/06/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. OBJECTIVE The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. METHODS We performed a systematic review of the literature in September 2020 by searching three databases-PubMed, Web of Science, and Scopus-using relevant keywords, such as "social media," "online health communities," "machine learning," "data mining," etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. RESULTS The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. CONCLUSIONS Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.
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Affiliation(s)
- Tavleen Singh
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, United States
| | - Kirk Roberts
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, United States
| | - Trevor Cohen
- Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
| | - Nathan Cobb
- Georgetown University Medical Center, Washington, DC, United States
| | - Jing Wang
- School of Nursing, The University of Texas Health Science Center, San Antonio, TX, United States
| | - Kayo Fujimoto
- School of Public Health, The University of Texas Health Science Center, Houston, TX, United States
| | - Sahiti Myneni
- School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, United States
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