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O'Connor K, Golder S, Weissenbacher D, Klein AZ, Magge A, Gonzalez-Hernandez G. Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review. J Med Internet Res 2024; 26:e47923. [PMID: 38488839 PMCID: PMC10980991 DOI: 10.2196/47923] [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: 04/05/2023] [Revised: 07/28/2023] [Accepted: 08/01/2023] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. OBJECTIVE This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. METHODS We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. RESULTS Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. CONCLUSIONS Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.
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Affiliation(s)
- Karen O'Connor
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Su Golder
- Department of Health Sciences, University of York, York, United Kingdom
| | - Davy Weissenbacher
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Ari Z Klein
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Arjun Magge
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Javadi V, Kamfar S, Zeinali V, Rahmani K, Moghaddamemami FH. Online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP): an infodemiology study. BMC Health Serv Res 2023; 23:1389. [PMID: 38082454 PMCID: PMC10714479 DOI: 10.1186/s12913-023-10357-2] [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: 07/09/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUNDS Previous studies have indicated that users' health information-seeking behavior can serve as a reflection of current health issues within a community. This study aimed to investigate the online information-seeking behavior of Iranian web users on Google about Henoch-Schönlein purpura (HSP). METHODS Google Trends (GTr) was utilized to collect big data from the internet searches conducted by Iranian web users. A focus group discussion was employed to identify users' selected keywords when searching for HSP. Additionally, keywords related to the disease's symptoms were selected based on recent clinical studies. All keywords were queried in GTr from January 1, 2012 to October 30, 2022. The outputs were saved in an Excel format and analyzed using SPSS. RESULTS The highest and lowest search rates of HSP were recorded in winter and summer, respectively. There was a significant positive correlation between HSP search rates and the terms "joint pain" (P = 0.007), "vomiting" (P = 0.032), "hands and feet swelling" (P = 0.041) and "seizure" (P < 0.001). CONCLUSION The findings were in accordance with clinical facts about HSP, such as its seasonal pattern and accompanying symptoms. It appears that the information-seeking behavior of Iranian users regarding HSP can provide valuable insights into the outbreak of this disease in Iran.
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Grants
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- 18441 Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Pediatric Pathology Research Center, Research Institute for Children’s Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Affiliation(s)
- Vadood Javadi
- Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Sharareh Kamfar
- Pediatric Congenital Hematologic Disorders Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Vahide Zeinali
- Pediatric Pathology Research Center, Research Institute for Children's Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Khosro Rahmani
- Department of pediatric rheumatology, Shahid Beheshti University of Medical Sciences, Mofid children's Hospital, Tehran, Iran
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Young LE, Nan Y, Jang E, Stevens R. Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review. Curr HIV/AIDS Rep 2023; 20:470-480. [PMID: 37917386 PMCID: PMC10719139 DOI: 10.1007/s11904-023-00673-x] [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] [Accepted: 10/11/2023] [Indexed: 11/04/2023]
Abstract
PURPOSE OF REVIEW The purpose of this scoping review was to summarize literature regarding the use of user-generated digital data collected for non-epidemiological purposes in human immunodeficiency virus (HIV) research. RECENT FINDINGS Thirty-nine papers were included in the final review. Four types of digital data were used: social media data, web search queries, mobile phone data, and data from global positioning system (GPS) devices. With these data, four HIV epidemiological objectives were pursued, including disease surveillance, behavioral surveillance, assessment of public attention to HIV, and characterization of risk contexts. Approximately one-third used machine learning for classification, prediction, or topic modeling. Less than a quarter discussed the ethics of using user-generated data for epidemiological purposes. User-generated digital data can be used to monitor, predict, and contextualize HIV risk and can help disrupt trajectories of risk closer to onset. However, more attention needs to be paid to digital ethics and the direction of the field in a post-Application Programming Interface (API) world.
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Affiliation(s)
- Lindsay E Young
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA.
| | - Yuanfeixue Nan
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA
| | - Eugene Jang
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA
| | - Robin Stevens
- Annenberg School for Communication and Journalism, University of Southern California, 3502 Watt Way, Los Angeles, CA, 90089, USA
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Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. INTERNATIONAL JOURNAL OF DIGITAL EARTH 2023; 16:130-157. [PMID: 37997607 PMCID: PMC10664840 DOI: 10.1080/17538947.2022.2161652] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Accepted: 12/17/2022] [Indexed: 11/25/2023]
Abstract
Geospatial social media (GSM) data has been increasingly used in public health due to its rich, timely, and accessible spatial information, particularly in infectious disease research. This review synthesized 86 research articles that use GSM data in infectious diseases published between December 2013 and March 2022. These articles cover 12 infectious disease types ranging from respiratory infectious diseases to sexually transmitted diseases with spatial levels varying from the neighborhood, county, state, and country. We categorized these studies into three major infectious disease research domains: surveillance, explanation, and prediction. With the assistance of advanced statistical and spatial methods, GSM data has been widely and deeply applied to these domains, particularly in surveillance and explanation domains. We further identified four knowledge gaps in terms of contextual information use, application scopes, spatiotemporal dimension, and data limitations and proposed innovation opportunities for future research. Our findings will contribute to a better understanding of using GSM data in infectious diseases studies and provide insights into strategies for using GSM data more effectively in future research.
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Affiliation(s)
- Fengrui Jing
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Zhenlong Li
- Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC, USA
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
| | - Shan Qiao
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Jiajia Zhang
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Banky Olatosi
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
| | - Xiaoming Li
- Big Data Health Science Center, University of South Carolina, Columbia, SC, USA
- Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA
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Burgess R, Feliciano JT, Lizbinski L, Ransome Y. Trends and Characteristics of #HIVPrevention Tweets Posted Between 2014 and 2019: Retrospective Infodemiology Study. JMIR Public Health Surveill 2022; 8:e35937. [PMID: 35969453 PMCID: PMC9412898 DOI: 10.2196/35937] [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: 12/23/2021] [Revised: 06/24/2022] [Accepted: 07/11/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Twitter is becoming an increasingly important avenue for people to seek information about HIV prevention. Tweets about HIV prevention may reflect or influence current norms about the acceptability of different HIV prevention methods. Therefore, it may be useful to empirically investigate trends in the level of attention paid to different HIV prevention topics on Twitter over time. OBJECTIVE The primary objective of this study was to investigate temporal trends in the frequency of tweets about different HIV prevention topics on Twitter between 2014 and 2019. METHODS We used the Twitter application programming interface to obtain English-language tweets employing #HIVPrevention between January 1, 2014, and December 31, 2019 (n=69,197, globally). Using iterative qualitative content analysis on samples of tweets, we developed a keyword list to categorize the tweets into 10 prevention topics (eg, condom use, preexposure prophylaxis [PrEP]) and compared the frequency of tweets mentioning each topic over time. We assessed the overall change in the proportions of #HIVPrevention tweets mentioning each prevention topic in 2019 as compared with 2014 using chi-square and Fisher exact tests. We also conducted descriptive analyses to identify the accounts posting the most original tweets, the accounts retweeted most frequently, the most frequently used word pairings, and the spatial distribution of tweets in the United States compared with the number of state-level HIV cases. RESULTS PrEP (13,895 tweets; 20.08% of all included tweets) and HIV testing (7688, 11.11%) were the most frequently mentioned topics, whereas condom use (2941, 4.25%) and postexposure prophylaxis (PEP; 823, 1.19%) were mentioned relatively less frequently. The proportions of tweets mentioning PrEP (327/2251, 14.53%, in 2014, 5067/12,971, 39.1%, in 2019; P≤.001), HIV testing (208/2251, 9.24%, in 2014, 2193/12,971, 16.91% in 2019; P≤.001), and PEP (25/2251, 1.11%, in 2014, 342/12,971, 2.64%, in 2019; P≤.001) were higher in 2019 compared with 2014, whereas the proportions of tweets mentioning abstinence, condom use, circumcision, harm reduction, and gender inequity were lower in 2019 compared with 2014. The top retweeted accounts were mostly UN-affiliated entities; celebrities and HIV advocates were also represented. Geotagged #HIVPrevention tweets in the United States between 2014 and 2019 (n=514) were positively correlated with the number of state-level HIV cases in 2019 (r=0.81, P≤.01). CONCLUSIONS Twitter may be a useful source for identifying HIV prevention trends. During our evaluation period (2014-2019), the most frequently mentioned prevention topics were PrEP and HIV testing in tweets using #HIVPrevention. Strategic responses to these tweets that provide information about where to get tested or how to obtain PrEP may be potential approaches to reduce HIV incidence.
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Affiliation(s)
- Raquel Burgess
- Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, CT, United States
| | - Josemari T Feliciano
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, United States
| | - Leonardo Lizbinski
- Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, CT, United States
- The Warren Alpert Medical School, Brown University, Providence, RI, United States
| | - Yusuf Ransome
- Department of Social and Behavioral Sciences, Yale School of Public Health, Yale University, New Haven, CT, United States
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Weissenbacher D, Flores JI, Wang Y, O’Connor K, Rawal S, Stevens R, Gonzalez-Hernandez G. Automatic Cohort Determination from Twitter for HIV Prevention amongst Black and Hispanic Men. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2022:504-513. [PMID: 35854738 PMCID: PMC9285152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recruiting people from diverse backgrounds to participate in health research requires intentional and culture-driven strategic efforts. In this study, we utilize publicly available Twitter posts to identify targeted populations to recruit for our HIV prevention study. Natural language processing and machine learning classification methods were used to find self-declarations of ethnicity, gender, age group, and sexually-explicit language. Using the official Twitter API we collected 47.4 million tweets posted over 8 months from two areas geo-centered around Los Angeles. Using available tools (Demographer and M3), we identified the age and race of 5,392 users as likely young Black or Hispanic men living in Los Angeles. We then collected and analyzed their timelines to automatically find sex-related tweets, yielding 2,166 users. Despite a limited precision, our results suggest that it is possible to automatically identify users based on their demographic attributes and Twitter language characteristics for enrollment into epidemiological studies.
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Affiliation(s)
| | - J. Ivan Flores
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yunwen Wang
- University of Southern California, Los Angeles, California, USA
| | - Karen O’Connor
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - Robin Stevens
- University of Southern California, Los Angeles, California, USA
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Grabar N, Grouin C. Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing. Yearb Med Inform 2021; 30:257-263. [PMID: 34479397 PMCID: PMC8416212 DOI: 10.1055/s-0041-1726528] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Objectives:
To analyze the content of publications within the medical NLP domain in 2020.
Methods:
Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.
Results:
Three best papers have been selected in 2020. We also propose an analysis of the content of the NLP publications in 2020, all topics included.
Conclusion:
The two main issues addressed in 2020 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as diversification of languages processed and use of information from social networks
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Affiliation(s)
- Natalia Grabar
- Université Paris Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France.,STL, CNRS, Université de Lille, Domaine du Pont-de-bois, Villeneuve-d'Ascq cedex, France
| | - Cyril Grouin
- Université Paris Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France
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Oh J, Bonett S, Kranzler EC, Saconi B, Stevens R. User and Message Level Correlates of Endorsement and Engagement for HIV-related Messages on Twitter: cross sectional study (Preprint). JMIR Public Health Surveill 2021; 8:e32718. [PMID: 35713945 PMCID: PMC9250060 DOI: 10.2196/32718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 03/16/2022] [Accepted: 05/10/2022] [Indexed: 11/13/2022] Open
Abstract
Background Youth and young adults continue to experience high rates of HIV and are also frequent users of social media. Social media platforms such as Twitter can bolster efforts to promote HIV prevention for these individuals, and while HIV-related messages exist on Twitter, little is known about the impact or reach of these messages for this population. Objective This study aims to address this gap in the literature by identifying user and message characteristics that are associated with tweet endorsement (favorited) and engagement (retweeted) among youth and young men (aged 13-24 years). Methods In a secondary analysis of data from a study of HIV-related messages posted by young men on Twitter, we used model selection techniques to examine user and tweet-level factors associated with tweet endorsement and engagement. Results Tweets from personal user accounts garnered greater endorsement and engagement than tweets from institutional users (aOR 3.27, 95% CI 2.75-3.89; P<.001). High follower count was associated with increased endorsement and engagement (aOR 1.05, 95% CI 1.04-1.06; P<.001); tweets that discussed STIs garnered lower endorsement and engagement (aOR 0.59, 95% CI 0.47-1.74; P<.001). Conclusions Findings suggest practitioners should partner with youth to design and disseminate HIV prevention messages on social media, incorporate content that resonates with youth audiences, and work to challenge stigma and foster social norms conducive to open conversation about sex, sexuality, and health.
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Affiliation(s)
- Jimin Oh
- Graduate School of Education, University of Pennsylvania, Philadelphia, PA, United States
| | - Stephen Bonett
- School of Nursing, University of Pennsylvania, Philadelphia, PA, United States
| | | | - Bruno Saconi
- School of Nursing, University of Pennsylvania, Philadelphia, PA, United States
| | - Robin Stevens
- Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, United States
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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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