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Liu Y, Wu J, Zhou J, Guo J, Liang C, Xing Y, Wang Z, Chen L, Ding Y, Ren D, Bai Y, Hu D. Identification of high-risk population of pneumoconiosis using deep learning segmentation of lung 3D images and radiomics texture analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 244:108006. [PMID: 38215580 DOI: 10.1016/j.cmpb.2024.108006] [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: 06/19/2023] [Revised: 12/25/2023] [Accepted: 01/01/2024] [Indexed: 01/14/2024]
Abstract
OBJECTION The aim of this study is to develop an early-warning model for identifying high-risk populations of pneumoconiosis by combining lung 3D images and radiomics lung texture features. METHODS A retrospective study was conducted, including 600 dust-exposed workers and 300 confirmed pneumoconiosis patients. Chest computed tomography (CT) images were divided into a training set and a test set in a 2:1 ratio. Whole-lung segmentation was performed using deep learning models for feature extraction of radiomics. Two feature selection algorithms and five classification models were used. The optimal model was selected using a 10-fold cross-validation strategy, and the calibration curve and decision curve were evaluated. To verify the applicability of the model, the diagnostic efficiency and accuracy between the model and human interpretation were compared. Additionally, the risk probabilities for different risk groups defined by the model were compared at different time intervals. RESULTS Four radiomics features were ultimately used to construct the predictive model. The logistic regression model was the most stable in both the training set and testing set, with an area under curve (AUC) of 0.964 (95 % confidence interval [CI], 0.950-0.976) and 0.947 (95 %CI, 0.925-0.964). In the training and testing sets, the Brier scores were 0.092 and 0.14, respectively, with threshold probability ranges of 2 %-99 % and 2 %-85 %. These results indicate that the model exhibits good calibration and clinical benefit. The comparison between the model and human interpretation showed that the model was not inferior in terms of diagnostic efficiency and accuracy. Additionally, the high-risk population identified by the model was diagnosed as pneumoconiosis two years later. CONCLUSION This study provides a meticulous and quantifiable method for detecting and assessing the risk of pneumoconiosis, building upon accurate diagnosis. Employing risk scoring and probability estimation, not only enhances the efficiency of diagnostic physicians but also provides a valuable reference for controlling the occurrence of pneumoconiosis.
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Affiliation(s)
- Yafeng Liu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Jing Wu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China.
| | - Jiawei Zhou
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Jianqiang Guo
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Chao Liang
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Yingru Xing
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Department of Clinical Laboratory, Anhui Zhongke Gengjiu Hospital, Hefei, PR China
| | - Zhongyu Wang
- Ziwei King Star Digital Technology Co., Ltd., Hefei, PR China
| | - Lijuan Chen
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China
| | - Yan Ding
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China
| | - Dingfei Ren
- Occupational Control Hospital of Huaihe Energy Group, Huainan, PR China.
| | - Ying Bai
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China
| | - Dong Hu
- School of Medicine, Anhui University of Science and Technology, Huainan, PR China; Anhui Province Engineering Laboratory of Occupational Health and Safety, Anhui University of Science and Technology, Huainan, PR China; Key Laboratory of Industrial Dust Prevention and Control & Occupational Safety and Health of the Ministry of Education, Anhui University of Science and Technology, Huainan, PR China.
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Kong G, Schott AS, Lee J, Dashtian H, Murthy D. Understanding e-cigarette content and promotion on YouTube through machine learning. Tob Control 2023; 32:739-746. [PMID: 35504690 PMCID: PMC9630169 DOI: 10.1136/tobaccocontrol-2021-057243] [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: 12/27/2021] [Accepted: 04/19/2022] [Indexed: 11/04/2022]
Abstract
INTRODUCTION YouTube is a popular social media used by youth and has electronic cigarette (e-cigarette) content. We used machine learning to identify the content of e-cigarette videos, featured e-cigarette products, video uploaders, and marketing and sales of e-cigarette products. METHODS We identified e-cigarette content using 18 search terms (eg, e-cig) using fictitious youth viewer profiles and predicted four models using the metadata as the input to supervised machine learning: (1) video themes, (2) featured e-cigarette products, (3) channel type (ie, video uploaders) and (4) discount/sales. We assessed the association between engagement data and the four models. RESULTS 3830 English videos were included in the supervised machine learning. The most common video theme was 'product review' (48.9%), followed by 'instruction' (eg, 'how to' use/modify e-cigarettes; 17.3%); diverse e-cigarette products were featured; 'vape enthusiasts' most frequently posted e-cigarette videos (54.0%), followed by retailers (20.3%); 43.2% of videos had discount/sales of e-cigarettes; and the most common sales strategy was external links for purchasing (34.1%). 'Vape trick' was the least common theme but had the highest engagement (eg, >2 million views). 'Cannabis' (53.9%) and 'instruction' (49.9%) themes were more likely to have external links for purchasing (p<0.001). The four models achieved an F1 score (a measure of model accuracy) of up to 0.87. DISCUSSION Our findings indicate that on YouTube videos accessible to youth, a variety of e-cigarette products are featured through diverse videos themes, with discount/sales. The findings highlight the need to regulate the promotion of e-cigarettes on social media platforms.
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Affiliation(s)
- Grace Kong
- Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Juhan Lee
- Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Hassan Dashtian
- The School of Journalism, The University of Texas at Austin, Austin, Texas, USA
| | - Dhiraj Murthy
- The School of Journalism, The University of Texas at Austin, Austin, Texas, USA
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Fisher A, Young MM, Payer D, Pacheco K, Dubeau C, Mago V. Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework. J Med Internet Res 2023; 25:e43630. [PMID: 37725410 PMCID: PMC10548323 DOI: 10.2196/43630] [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: 10/18/2022] [Revised: 04/21/2023] [Accepted: 08/18/2023] [Indexed: 09/21/2023] Open
Abstract
BACKGROUND A hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada. OBJECTIVE The goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities. METHODS To collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2. RESULTS When looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of ~84.5%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of ~94.1%) with the subject matter experts. CONCLUSIONS This study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain.
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Affiliation(s)
- Andrew Fisher
- Department of Mathematics and Computing Science, Saint Mary's University, Halifax, NS, Canada
| | - Matthew Maclaren Young
- Canadian Centre on Substance Use and Addiction, Ottawa, ON, Canada
- Greo Evidence Insights, Guelph, ON, Canada
- Department of Psychology, Carleton University, Ottawa, ON, Canada
| | - Doris Payer
- Canadian Centre on Substance Use and Addiction, Ottawa, ON, Canada
| | - Karen Pacheco
- Canadian Medical Protective Association, Ottawa, ON, Canada
| | - Chad Dubeau
- Canadian Centre on Substance Use and Addiction, Ottawa, ON, Canada
| | - Vijay Mago
- Faculty of Health, York University, Toronto, ON, Canada
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Dupuy-Zini A, Audeh B, Gérardin C, Duclos C, Gagneux-Brunon A, Bousquet C. Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts. J Med Internet Res 2023; 25:e37237. [PMID: 36596215 PMCID: PMC10132828 DOI: 10.2196/37237] [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: 02/11/2022] [Revised: 07/17/2022] [Accepted: 08/09/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France. OBJECTIVE This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis. METHODS This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets. RESULTS A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies. CONCLUSIONS Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.
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Affiliation(s)
- Alexandre Dupuy-Zini
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
| | - Bissan Audeh
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
| | - Christel Gérardin
- Institut Pierre Louis d'Epidémiologie et de Santé Publique, Département de médecine interne, Sorbonne Université, Paris, France
| | - Catherine Duclos
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
| | - Amandine Gagneux-Brunon
- Groupe sur l'Immunité des Muqueuses et Agents Pathogènes, Centre International de Recherche en Infectiologie, University of Lyon, Saint Etienne, France
- Vaccinologie, Centre Hospitalier Universitaire de Saint-Etienne, Saint Etienne, France
| | - Cedric Bousquet
- Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Sorbonne Université, Université Sorbonne Paris Nord, Institut national de la santé et de la recherche médicale, INSERM, Paris, France
- Service de santé publique et information médicale, Centre Hospitalier Universitaire de Saint Etienne, Saint Etienne, France
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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: 2] [Impact Index Per Article: 2.0] [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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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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Leung T, Kasson E, Singh AK, Ren Y, Kaiser N, Huang M, Cavazos-Rehg PA. Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use-Associated Lung Injury Outbreak: Comparative Study. J Med Internet Res 2022; 24:e39460. [PMID: 36512403 PMCID: PMC9795395 DOI: 10.2196/39460] [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: 05/11/2022] [Revised: 09/16/2022] [Accepted: 10/29/2022] [Indexed: 11/05/2022] Open
Abstract
BACKGROUND Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use-associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. OBJECTIVE This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. METHODS Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). RESULTS Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P<.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P<.001, 95% CI -0.4289 to -0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. CONCLUSIONS Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms.
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Affiliation(s)
| | - Erin Kasson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Avineet Kumar Singh
- Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States
| | - Yang Ren
- Department of Integrated Information Technology, University of South Carolina, Columbia, SC, United States
| | - Nina Kaiser
- 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 A Cavazos-Rehg
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
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Ezike NC, Ames Boykin A, Dobbs PD, Mai H, Primack BA. Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series. JMIR INFODEMIOLOGY 2022; 2:e37412. [PMID: 37113447 PMCID: PMC9987194 DOI: 10.2196/37412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 06/04/2022] [Accepted: 06/17/2022] [Indexed: 04/29/2023]
Abstract
Background Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. Objective This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. Results When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. Conclusions e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States.
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Affiliation(s)
- Nnamdi C Ezike
- College of Education and Health Professions University of Arkansas Fayetteville, AR United States
| | - Allison Ames Boykin
- College of Education and Health Professions University of Arkansas Fayetteville, AR United States
| | - Page D Dobbs
- College of Education and Health Professions University of Arkansas Fayetteville, AR United States
| | - Huy Mai
- College of Engineering University of Arkansas Fayetteville, AR United States
| | - Brian A Primack
- College of Public Health and Human Sciences Oregon State University Corvallis, OR United States
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Sauvayre R, Vernier J, Chauvière C. Using supervised learning to analyze the French vaccine debate on Twitter. JMIR Med Inform 2022; 10:e37831. [PMID: 35512274 PMCID: PMC9116457 DOI: 10.2196/37831] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/01/2022] [Accepted: 05/04/2022] [Indexed: 11/13/2022] Open
Abstract
Background As the COVID-19 pandemic progressed, disinformation, fake news, and conspiracy theories spread through many parts of society. However, the disinformation spreading through social media is, according to the literature, one of the causes of increased COVID-19 vaccine hesitancy. In this context, the analysis of social media posts is particularly important, but the large amount of data exchanged on social media platforms requires specific methods. This is why machine learning and natural language processing models are increasingly applied to social media data. Objective The aim of this study is to examine the capability of the CamemBERT French-language model to faithfully predict the elaborated categories, with the knowledge that tweets about vaccination are often ambiguous, sarcastic, or irrelevant to the studied topic. Methods A total of 901,908 unique French-language tweets related to vaccination published between July 12, 2021, and August 11, 2021, were extracted using Twitter’s application programming interface (version 2; Twitter Inc). Approximately 2000 randomly selected tweets were labeled with 2 types of categorizations: (1) arguments for (pros) or against (cons) vaccination (health measures included) and (2) type of content (scientific, political, social, or vaccination status). The CamemBERT model was fine-tuned and tested for the classification of French-language tweets. The model’s performance was assessed by computing the F1-score, and confusion matrices were obtained. Results The accuracy of the applied machine learning reached up to 70.6% for the first classification (pro and con tweets) and up to 90% for the second classification (scientific and political tweets). Furthermore, a tweet was 1.86 times more likely to be incorrectly classified by the model if it contained fewer than 170 characters (odds ratio 1.86; 95% CI 1.20-2.86). Conclusions The accuracy of the model is affected by the classification chosen and the topic of the message examined. When the vaccine debate is jostled by contested political decisions, tweet content becomes so heterogeneous that the accuracy of the model drops for less differentiated classes. However, our tests showed that it is possible to improve the accuracy by selecting tweets using a new method based on tweet length.
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Affiliation(s)
- Romy Sauvayre
- Laboratoire de Psychologie Sociale et Cognitive, Université Clermont Auvergne, CNRS, Clermont-Ferrand, FR.,Polytech Clermont, 2 avenue Blaise Pascal, Aubiere, FR
| | - Jessica Vernier
- Laboratoire de Psychologie Sociale et Cognitive, Université Clermont Auvergne, CNRS, Clermont-Ferrand, FR
| | - Cédric Chauvière
- Laboratoire de Mathématiques Blaise Pascal, Université Clermont Auvergne, CNRS, Clermont-Ferrand, FR.,Polytech Clermont, 2 avenue Blaise Pascal, Aubiere, FR
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10
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Chu KH, Hershey TB, Hoffman BL, Wolynn R, Colditz JB, Sidani JE, Primack BA. Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets. J Med Internet Res 2022; 24:e27894. [PMID: 35333188 PMCID: PMC8994141 DOI: 10.2196/27894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 04/13/2021] [Accepted: 02/03/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Puff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices. OBJECTIVE This study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes. METHODS Of 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy. RESULTS Of the human-coded tweets, 2123 (80.2%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9%) discussed tobacco policy, including flavors (n=320, 45.9%), regulations (n=124, 17.8%), purchases (n=117, 16.8%), and other products (n=110, 15.8%). Approximately 22% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9%), appetite suppression (n=59, 12.3%), frequent use (n=47, 9.8%), and self-reported dependence (n=110, 22.9%). CONCLUSIONS This study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand.
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Affiliation(s)
- Kar-Hai Chu
- Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Tina B Hershey
- Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Beth L Hoffman
- Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Riley Wolynn
- Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jason B Colditz
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Jaime E Sidani
- Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States
| | - Brian A Primack
- College of Education and Health Professions, University of Arkansas, Fayetteville, AR, United States
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11
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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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12
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Hudon A, Beaudoin M, Phraxayavong K, Dellazizzo L, Potvin S, Dumais A. Use of Automated Thematic Annotations for Small Data Sets in a Psychotherapeutic Context: Systematic Review of Machine Learning Algorithms. JMIR Ment Health 2021; 8:e22651. [PMID: 34677133 PMCID: PMC8571689 DOI: 10.2196/22651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 10/06/2020] [Accepted: 07/27/2021] [Indexed: 11/21/2022] Open
Abstract
BACKGROUND A growing body of literature has detailed the use of qualitative analyses to measure the therapeutic processes and intrinsic effectiveness of psychotherapies, which yield small databases. Nonetheless, these approaches have several limitations and machine learning algorithms are needed. OBJECTIVE The objective of this study is to conduct a systematic review of the use of machine learning for automated text classification for small data sets in the fields of psychiatry, psychology, and social sciences. This review will identify available algorithms and assess if automated classification of textual entities is comparable to the classification done by human evaluators. METHODS A systematic search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from their inception dates to 2021. The fields of psychiatry, psychology, and social sciences were selected as they include a vast array of textual entities in the domain of mental health that can be reviewed. Additional records identified through cross-referencing were used to find other studies. RESULTS This literature search identified 5442 articles that were eligible for our study after the removal of duplicates. Following abstract screening, 114 full articles were assessed in their entirety, of which 107 were excluded. The remaining 7 studies were analyzed. Classification algorithms such as naive Bayes, decision tree, and support vector machine classifiers were identified. Support vector machine is the most used algorithm and best performing as per the identified articles. Prediction classification scores for the identified algorithms ranged from 53%-91% for the classification of textual entities in 4-7 categories. In addition, 3 of the 7 studies reported an interjudge agreement statistic; these were consistent with agreement statistics for text classification done by human evaluators. CONCLUSIONS A systematic review of available machine learning algorithms for automated text classification for small data sets in several fields (psychiatry, psychology, and social sciences) was conducted. We compared automated classification with classification done by human evaluators. Our results show that it is possible to automatically classify textual entities of a transcript based solely on small databases. Future studies are nevertheless needed to assess whether such algorithms can be implemented in the context of psychotherapies.
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Affiliation(s)
- Alexandre Hudon
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Mélissa Beaudoin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | | | - Laura Dellazizzo
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Stéphane Potvin
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Alexandre Dumais
- Centre de recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montréal, QC, Canada
- Department of Psychiatry and Addictology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
- Services et Recherches Psychiatriques AD, Montréal, QC, Canada
- Institut national de psychiatrie légale Philippe-Pinel, Montréal, QC, Canada
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13
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Kasson E, Singh AK, Huang M, Wu D, Cavazos-Rehg P. Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak. Int J Med Inform 2021; 155:104574. [PMID: 34592539 DOI: 10.1016/j.ijmedinf.2021.104574] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 08/30/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022]
Abstract
INTRODUCTION Vaping product use (i.e., e-cigarettes) has been rising since 2000 in the United States. Negative health outcomes associated with vaping products have created public uncertainty and debates on social media platforms. This study explores the feasibility of using social media as a surveillance tool to identify relevant posts and at-risk vaping users. METHODS Using an interdisciplinary method that leverages natural language processing and manual content analysis, we extracted and analyzed 794,620 vaping-related tweets on Twitter. After observing significant increases in vaping-related tweets in July, August, and September 2019, additional human coding was completed on a subset of these tweets to better understand primary themes of vaping-related discussions on Twitter during this time frame. RESULTS We found significant increases in tweets related to negative health outcomes such as acute lung injury and respiratory issues during the outbreak of e-cigarette/vaping associated lung injury (EVALI) in the fall of 2019. Positive sentiment toward vaping remained high, even across the peak of this outbreak in July, August, and September. Tweets mentioning the public perceptions of youth risk were concerning, as were increases in marketing and marijuana-related tweets during this time. DISCUSSION The preliminary results of this study suggest the feasibility of using Twitter as a means of surveillance for public health crises, and themes found in this research could aid in specifying those groups or populations at risk on Twitter. As such, we plan to build automatic detection algorithms to identify these unique vaping users to connect them with a digital intervention in the future.
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Affiliation(s)
- Erin Kasson
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, USA
| | - Avineet Kumar Singh
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, USA
| | - Ming Huang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Dezhi Wu
- Department of Integrated Information Technology, University of South Carolina, Columbia, SC, USA.
| | - Patricia Cavazos-Rehg
- Department of Psychiatry, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO, USA
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14
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Baker W, Colditz JB, Dobbs PD, Mai H, Visweswaran S, Zhan J, Primack BA. Classification of Twitter Vaping Discourse Using BERTweet: Comparative Deep Learning Study (Preprint). JMIR Med Inform 2021; 10:e33678. [PMID: 35862172 PMCID: PMC9353682 DOI: 10.2196/33678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 03/21/2022] [Accepted: 05/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Objective Methods Results Conclusions
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Affiliation(s)
- William Baker
- Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR, United States
| | - Jason B Colditz
- Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States
| | - Page D Dobbs
- Health, Human Performance and Recreation Department, University of Arkansas, Fayetteville, AR, United States
| | - Huy Mai
- Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR, United States
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Justin Zhan
- Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR, United States
| | - Brian A Primack
- College of Public Health and Human Science, Oregon State University, Corvallis, OR, United States
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15
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Hswen Y, Yom-Tov E. Analysis of a Vaping-Associated Lung Injury Outbreak through Participatory Surveillance and Archival Internet Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18158203. [PMID: 34360495 PMCID: PMC8346109 DOI: 10.3390/ijerph18158203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/28/2021] [Accepted: 07/30/2021] [Indexed: 11/22/2022]
Abstract
The US Centers for Disease Control and Prevention alerted of a suspected outbreak of lung illness associated with using E-cigarette products in September 2019. At the time that the CDC published its alert little was known about the causes of the outbreak or who was at risk for it. Here we provide insights into the outbreak through analysis of passive reporting and participatory surveillance. We collected data about vaping habits and associated adverse reactions from four data sources pertaining to people in the USA: A participatory surveillance platform (YouVape), Reddit, Google Trends, and Bing. Data were analyzed to identify vaping behaviors and reported adverse events. These were correlated among sources and with prior reports. Data was obtained from 720 YouVape users, 4331 Reddit users, and over 1 million Bing users. Large geographic variation was observed across vaping products. Significant correlation was found among the data sources in reported adverse reactions. Models of participatory surveillance data found specific product and adverse reaction associations. Specifically, cannabidiol was found to be associated with fever, while tetrahydrocannabinol was found to be correlated with diarrhea. Our results demonstrate that utilization of different, complementary, online data sources provide a holistic view of vaping associated lung injury while augmenting traditional data sources.
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Affiliation(s)
- Yulin Hswen
- Department of Epidemiology and Biostatistics, University of California at San Francisco, San Francisco, CA 94158, USA;
- Bakar Computational Health Sciences Institute, University of California at San Francisco, San Francisco, CA 94143, USA
- Innovation Program, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elad Yom-Tov
- Microsoft Research Israel, 3 Alan Turing Str., Herzeliya 4672415, Israel
- Faculty of Industrial Engineering and Management, Technion, Haifa 3200000, Israel
- Correspondence:
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16
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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: 11] [Impact Index Per Article: 2.8] [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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