SynthesisJournal of medical Internet research2017
Methods for Coding Tobacco-Related Twitter Data: A Systematic Review.
Synthesis in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Factors Influencing the Initiation and Continued Engagement of Digital Mental Health Tools Among Adults: Theory of Planned Behavior-Informed Systematic Review.JMIR mental health · 2026Pooled it
- Examining Tobacco-Related Social Media Research in Government Policy Documents: Systematic Review.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Pooled it
- Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.Frontiers in public health · 2021Pooled it
- AI for Tobacco Control: Identifying Tobacco-Promoting Social Media Content Using Large Language Models.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2025Article
- Examining the Peer-Reviewed Literature on Tobacco-Related Social Media Data: Scoping Review.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Analysis of the hikikomori phenomenon - an international infodemiology study of Twitter data in Portuguese.BMC public health · 2024Article
- Twitter Misinformation Discourses About Vaping: Systematic Content Analysis.Journal of medical Internet research · 2023Article
- Public Perceptions of the Food and Drug Administration's Proposed Rules Prohibiting Menthol Cigarettes on Twitter: Observational Study.JMIR formative research · 2023Article
- Topics and Sentiment Surrounding Vaping on Twitter and Reddit During the 2019 e-Cigarette and Vaping Use-Associated Lung Injury Outbreak: Comparative Study.Journal of medical Internet research · 2022Article
- Potential Impact of FDA Flavor Enforcement Policy on Vaping Behavior on Twitter.International journal of environmental research and public health · 2022Article
- Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review.Journal of medical Internet research · 2022Article
- Discussions and Misinformation About Electronic Nicotine Delivery Systems and COVID-19: Qualitative Analysis of Twitter Content.JMIR formative research · 2022Article
- Perception of the Food and Drug Administration Electronic Cigarette Flavor Enforcement Policy on Twitter: Observational Study.JMIR public health and surveillance · 2022Observational
- Public Reactions to the New York State Policy on Flavored Electronic Cigarettes on Twitter: Observational Study.JMIR public health and surveillance · 2022Observational
- Examining Twitter Discourse on Electronic Cigarette and Tobacco Consumption During National Cancer Prevention Month in 2018: Topic Modeling and Geospatial Analysis.Journal of medical Internet research · 2021Article
- Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak.International journal of medical informatics · 2021Article
- Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study.JMIR research protocols · 2021Article
- Eating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter.Journal of medical Internet research · 2020Article
- User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study.Journal of medical Internet research · 2020Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundAs Twitter has grown in popularity to 313 million monthly active users, researchers have increasingly been using it as a data source for tobacco-related research.
objectiveThe objective of this systematic review was to assess the methodological approaches of categorically coded tobacco Twitter data and make recommendations for future studies.
methodsData sources included PsycINFO, Web of Science, PubMed, ABI/INFORM, Communication Source, and Tobacco Regulatory Science. Searches were limited to peer-reviewed journals and conference proceedings in English from January 2006 to July 2016. The initial search identified 274 articles using a Twitter keyword and a tobacco keyword. One coder reviewed all abstracts and identified 27 articles that met the following inclusion criteria: (1) original research, (2) focused on tobacco or a tobacco product, (3) analyzed Twitter data, and (4) coded Twitter data categorically. One coder extracted data collection and coding methods.
resultsE-cigarettes were the most common type of Twitter data analyzed, followed by specific tobacco campaigns. The most prevalent data sources were Gnip and Twitter's Streaming application programming interface (API). The primary methods of coding were hand-coding and machine learning. The studies predominantly coded for relevance, sentiment, theme, user or account, and location of user.
conclusionsStandards for data collection and coding should be developed to be able to more easily compare and replicate tobacco-related Twitter results. Additional recommendations include the following: sample Twitter's databases multiple times, make a distinction between message attitude and emotional tone for sentiment, code images and URLs, and analyze user profiles. Being relatively novel and widely used among adolescents and black and Hispanic individuals, Twitter could provide a rich source of tobacco surveillance data among vulnerable populations.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.