Methods for Coding Tobacco-Related Twitter Data: A Systematic Review
Open accessAbstract
BACKGROUND: As 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. OBJECTIVE: The objective of this systematic review was to assess the methodological approaches of categorically coded tobacco Twitter data and make recommendations for future studies. METHODS: Data 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. RESULTS: E-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. CONCLUSIONS: Standards 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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The single coder approach allowed for consistency of coding and interpretation of codes.
“As we used emergent coding and there was no a priori codebook, a single coder approach also allowed for consistency of coding and interpretation of codes, and this approach has been used in systematic reviews [21].”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [21]
The Review Limitations section (page unavailable) confirms that this systematic review used a single coder to select articles and extract data. It does not say that using one coder improved consistency of coding or interpretation; instead, it notes that decisions might have differed with another coder. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe risk of publication bias with significant results is small compared with a meta-analysis of outcomes.
“Therefore, the risk of publication bias with significant results is small compared with a meta-analysis of outcomes [21].”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [21]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Practical Statistics for Medical Research.
Bartko, J. J., & Altman, D. G. (1992) · Journal of the American Statistical Association · doi:10.2307/2290246 · Cited by 4,834 · see citing works
A multidimensional approach for detecting irony in Twitter
Reyes, A., Rosso, P., & Veale, T. (2012) · Language Resources and Evaluation · doi:10.1007/s10579-012-9196-x · Cited by 464 · see citing works
Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products
Myslín, M., Zhu, S., Chapman, W. W., & Conway, M. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2534 · Cited by 323 · see citing works
A cross-sectional examination of marketing of electronic cigarettes on Twitter
Huang, J., Kornfield, R., Szczypka, G., & Emery, S. (2014) · Tobacco Control · doi:10.1136/tobaccocontrol-2014-051551 · Cited by 271 · see citing works
Kim, Y., Huang, J., & Emery, S. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4738 · Cited by 178 · see citing works
Social Listening: A Content Analysis of E-Cigarette Discussions on Twitter
Cole-Lewis, H. J., Pugatch, J., Sanders, A. E., Varghese, A., Posada, S., Yun, C., Schwarz, M., & Augustson, E. M. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4969 · Cited by 161 · see citing works
Non-cigarette tobacco products: what have we learnt and where are we headed?
O’Connor, R. J. (2012) · Tobacco Control · doi:10.1136/tobaccocontrol-2011-050281 · Cited by 149 · see citing works
Identifying Health-Related Topics on Twitter
Prier, K., Smith, M. S., Giraud-Carrier, C. G., & Hanson, C. L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19656-0_4 · Cited by 135 · see citing works
Harris, J. K., Moreland‐Russell, S., Choucair, B., Mansour, R., Staub, M., & Simmons, K. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3622 · Cited by 134 · see citing works
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment
Sobhani, P., Mohammad, S., & Kiritchenko, S. (2016) · Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s16-2021 · Cited by 130 · see citing works
Social Computing, Behavioral-Cultural Modeling and Prediction
Salerno, J. J., Chai, S., Nau, D. S., & Yang, S. J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19656-0 · Cited by 128 · see citing works
Twitter=quitter? An analysis of Twitter quit smoking social networks
Prochaska, J. J., Pechmann, C., Kim, R., & Leonhardt, J. M. (2011) · Tobacco Control · doi:10.1136/tc.2010.042507 · Cited by 110 · see citing works
Campaigns and counter campaigns: reactions on Twitter to e-cigarette education
Allem, J.-P., Escobedo, P., Chu, K., Soto, D. W., Cruz, T. B., & Unger, J. B. (2016) · Tobacco Control · doi:10.1136/tobaccocontrol-2015-052757 · Cited by 89 · see citing works
Self-citationVape, quit, tweet? Electronic cigarettes and smoking cessation on Twitter
van der Tempel, J., Noormohamed, A., Schwartz, R. M., Norman, C. D., Malas, M., & Zawertailo, L. A. (2016) · International Journal of Public Health · doi:10.1007/s00038-016-0791-2 · Cited by 87 · see citing works
Are You Scared Yet? Evaluating Fear Appeal Messages in Tweets About the Tips Campaign
Emery, S., Szczypka, G., Abril, E. P., Kim, Y., & Vera, L. E. (2014) · Journal of Communication · doi:10.1111/jcom.12083 · Cited by 86 · see citing works
Kim, A. E., Hopper, T., Simpson, S. A., Nonnemaker, J. M., Lieberman, A., Hansen, H. M., Guillory, J. E., & Porter, L. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4466 · Cited by 82 · see citing works
Chung, J. E. (2016) · Journal of Health Communication · doi:10.1080/10810730.2015.1103332 · Cited by 77 · see citing works
Vaporous Marketing: Uncovering Pervasive Electronic Cigarette Advertisements on Twitter
Clark, E. M., Jones, C. A., Williams, J. R., Kurti, A. N., Norotsky, M. C., Danforth, C. M., & Dodds, P. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0157304 · Cited by 75 · see citing works
Hookah-Related Twitter Chatter: A Content Analysis
Krauss, M. J., Sowles, S. J., Moreno, M. A., Zewdie, K., Grucza, R. A., Bierut, L. J., & Cavazos‐Rehg, P. A. (2015) · Preventing Chronic Disease · doi:10.5888/pcd12.150140 · Cited by 70 · see citing works
Cole-Lewis, H. J., Varghese, A., Sanders, A. E., Schwarz, M., Pugatch, J., & Augustson, E. M. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4392 · Cited by 68 · see citing works
Diffusion of Messages from an Electronic Cigarette Brand to Potential Users through Twitter
Chu, K., Unger, J. B., Allem, J.-P., Pattarroyo, M., Soto, D. W., Cruz, T. B., Yang, H., Jiang, L., & Yang, C. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0145387 · Cited by 55 · see citing works
Self-citationAllem, J.-P., & Ferrara, E. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.6185 · Cited by 53 · see citing works
Duke, J. C., Hansen, H. M., Kim, A. E., Curry, L. E., & Allen, J. A. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3430 · Cited by 52 · see citing works
Toward automated e-cigarette surveillance: Spotting e-cigarette proponents on Twitter
Kavuluru, R., & Sabbir, A. K. M. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.03.006 · Cited by 46 · see citing works
van den Heerik, R. A. M., van Hooijdonk, C. M. J., Burgers, C. F., & Steen, G. J. (2016) · Health Communication · doi:10.1080/10410236.2016.1168000 · Cited by 40 · see citing works
Price-related promotions for tobacco products on Twitter
Jo, C. L., Kornfield, R., Kim, Y., Emery, S., & Ribisl, K. M. (2015) · Tobacco Control · doi:10.1136/tobaccocontrol-2015-052260 · Cited by 36 · see citing works
Rocheleau, M., Sadasivam, R. S., Baquis, K., Stahl, H., Kinney, R. L., Pagoto, S., & Houston, T. K. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3768 · Cited by 33 · see citing works
Twitter User Gender Inference Using Combined Analysis of Text and Image Processing
Sakaki, S., Miura, Y., Ma, X., Hattori, K., & Ohkuma, T. (2014) · Workshop on Vision and Language (VL) · doi:10.3115/v1/w14-5408 · Cited by 33 · see citing works
Aphinyanaphongs, Y., Lulejian, A., BROWN, D. P., Bonneau, R., & Krebs, P. M. (2015) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814749411_0044 · Cited by 30 · see citing works
Perceptions of Secondhand E-cigarette Aerosol among Twitter Users
Unger, J. B., Escobedo, P., Allem, J.-P., Soto, D. W., Chu, K., & Cruz, T. B. (2016) · Tobacco Regulatory Science · doi:10.18001/trs.2.2.5 · Cited by 22 · see citing works
Self-citationElectronic Cigarettes on Twitter – Spreading the Appeal of Flavors
Chu, K., Unger, J. B., Cruz, T. B., & Soto, D. W. (2015) · Tobacco Regulatory Science · doi:10.18001/trs.1.1.4 · Cited by 20 · see citing works
Self-citationUser and Content Characteristics of Public Tweets Referencing Little Cigars
Step, M. M., Bracken, C. C., Trapl, E., & Flocke, S. A. (2015) · American Journal of Health Behavior · doi:10.5993/ajhb.40.1.5 · Cited by 10 · see citing works
Communicating Genetics and Smoking Through Social Media: Are We There Yet?
de Viron, S., Suggs, L. S., Brand, A. M., & Oyen, H. J. V. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2653 · Cited by 9 · see citing works
Exploring Consumer Attitudes towards Hookah, Cigarettes, and Cigars Using Twitter
Jain, S., Zhu, S., & Conway, M. (2015) · Tobacco Regulatory Science · doi:10.18001/trs.1.3.1 · Cited by 5 · see citing works
Twitter users’ reaction to a chain pharmacy’s decision to end tobacco sales
McDaniel, P. A., Patzke, H., & Malone, R. E. (2015) · Tobacco Induced Diseases · doi:10.1186/s12971-015-0060-9 · Cited by 3 · see citing works
Flagged first, then by citations
1 of the 36 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [52]George D Mallery P SPSS for Windows step by step: A simple guide and reference. 11.0 update. 4th edition 2003 Boston, MA Allyn & Bacon
- [58]Burger JD Henderson J Kim G Zarrella G Discriminating gender on Twitter 2011 Proceedings of the Conference on Empirical Methods in Natural Language Processing July 2011 Edinburgh, Scotland, UK
- [60]Nguyen D Gravel R Trieschnigg D Meder T “How old do you think I am?”: a study of language and age in twitter 2013 Proceedings of the Seventh International AAAI Conference on WeblogsSocial Media July 2013 Cambridge, MA
Cited by
Sorted by citations
Borghouts, J., Eikey, E., Mark, G., De Leon, C., Schueller, S. M., Schneider, M., Stadnick, N., Zheng, K., Mukamel, D., & Sorkin, D. H. (2021) · Journal of Medical Internet Research · doi:10.2196/24387 · Cited by 1,069 · see citing works
“Therefore, the risk of publication bias with significant results is small compared with a meta-analysis of outcomes [21].”
MethodResultInfodemiology and Infoveillance: Scoping Review
Mavragani, A. (2020) · Journal of Medical Internet Research · doi:10.2196/16206 · Cited by 232 · see citing works
Association Between Exposure to Tobacco Content on Social Media and Tobacco Use
Donaldson, S. I., Dormanesh, A., Pérez, C., Majmundar, A., & Allem, J.-P. (2022) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2022.2223 · Cited by 215 · see citing works
McCausland, K., Maycock, B. R., Leaver, T. B., & Jancey, J. M. (2018) · Journal of Medical Internet Research · doi:10.2196/11953 · Cited by 162 · see citing works
Toward Real-Time Infoveillance of Twitter Health Messages
Colditz, J. B., Chu, K., Emery, S., Larkin, C. R., James, A. E., Welling, J. S., & Primack, B. A. (2018) · American Journal of Public Health · doi:10.2105/ajph.2018.304497 · Cited by 85 · see citing works
Self-citationDigital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews
Li, L., Novillo-Ortiz, D., Muscat, N. A., & Kostkova, P. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.645260 · Cited by 58 · see citing works
User Perceptions of Different Electronic Cigarette Flavors on Social Media: Observational Study
Lu, X., Chen, L., Yuan, J., Luo, J., Luo, J., Xie, Z., & Li, D. (2020) · Journal of Medical Internet Research · doi:10.2196/17280 · Cited by 56 · see citing works
I wake up and hit the JUUL: Analyzing Twitter for JUUL nicotine effects and dependence
Sidani, J. E., Colditz, J. B., Barrett, E. L., Shensa, A. S., Chu, K., James, A. E., & Primack, B. A. (2019) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2019.06.005 · Cited by 51 · see citing works
Self-citationTalking about Tobacco on Twitter Is Associated with Tobacco Product Use
Unger, J. B., Urman, R., Cruz, T. B., Majmundar, A., Barrington-Trimis, J., Pentz, M. A., & McConnell, R. (2018) · Preventive Medicine · doi:10.1016/j.ypmed.2018.06.006 · Cited by 46 · see citing works
Self-citationEating Disorder Awareness Campaigns: Thematic and Quantitative Analysis Using Twitter
Viguria, I., Álvarez-Mon, M. Á., Llavero-Valero, M., Asúnsolo, Á., Ortuño, F., & Álvarez‐Mon, M. (2020) · Journal of Medical Internet Research · doi:10.2196/17626 · Cited by 42 · see citing works
Chen, L., Lu, X., Yuan, J., Luo, J., Luo, J., Xie, Z., & Li, D. (2020) · Journal of Medical Internet Research · doi:10.2196/17496 · Cited by 38 · see citing works
Pereira-Sanchez, V., Álvarez-Mon, M., del Barco, A. A., Álvarez-Mon, M., & Teo, A. (2019) · Journal of Medical Internet Research · doi:10.2196/14167 · Cited by 34 · see citing works
Lu, X., Sun, L., Xie, Z., & Li, D. (2022) · JMIR Public Health and Surveillance · doi:10.2196/25697 · Cited by 32 · see citing works
Kasson, E. M., Singh, A. K., Huang, M. S., Wu, D., & Cavazos‐Rehg, P. A. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104574 · Cited by 28 · see citing works
Vaping associated with healthy food words: A content analysis of Twitter
Basáñez, T., Majmundar, A., Cruz, T. B., & Unger, J. B. (2018) · Addictive Behaviors Reports · doi:10.1016/j.abrep.2018.09.007 · Cited by 22 · see citing works
Self-citationMethods to Establish Race or Ethnicity of Twitter Users: Scoping Review
Golder, S., Stevens, R. C., O’Connor, K., James, R., & Gonzalez‐Hernandez, G. (2022) · Journal of Medical Internet Research · doi:10.2196/35788 · Cited by 21 · see citing works
National Institutes of Health Support of Digital Health Behavior Research
Riley, W. T., Oh, A., Aklin, W. M., & Wolff‐Hughes, D. L. (2019) · Health Education & Behavior · doi:10.1177/1090198119866644 · Cited by 19 · see citing works
The social media response to the rollout of legalized cannabis retail in Ontario, Canada
Aversa, J. M., Jacobson, J., Hernández, T., Cleave, E., Macdonald, M., & Dizonno, S. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102580 · Cited by 18 · see citing works
Sun, L., Lu, X., Xie, Z., & Li, D. (2021) · JMIR Public Health and Surveillance · doi:10.2196/25216 · Cited by 20 · see citing works
Social Bots for Online Public Health Interventions
Deb, A., Majmundar, A., Seo, S., Matsui, A., Tandon, R., Shen, Y., Allem, J.-P., & Ferrara, E. (2018) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam.2018.8508382 · Cited by 17 · see citing works
Wu, D., Kasson, E. M., Singh, A. K., Ren, Y., Kaiser, N., Huang, M. S., & Cavazos‐Rehg, P. A. (2022) · Journal of Medical Internet Research · doi:10.2196/39460 · Cited by 17 · see citing works
Examining the Peer-Reviewed Literature on Tobacco-Related Social Media Data: Scoping Review
Donaldson, S. I., Dormanesh, A., Majmundar, A., Pérez, C., Lopez, H. N., Saghian, M., Beard, T., Unger, J. B., & Allem, J.-P. (2023) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntad186 · Cited by 15 · see citing works
Self-citationInstagram as a research tool for examining tobacco-related content: A methodological review
Malik, A. N., Berggren, W., & Al-Busaidi, A. S. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.102008 · Cited by 15 · see citing works
Crocamo, C., Viviani, M., Bartoli, F., Carrà, G., & Pasi, G. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17051510 · Cited by 14 · see citing works
Examining Tobacco-Related Social Media Research in Government Policy Documents: Systematic Review
Beard, T., Donaldson, S. I., Unger, J. B., & Allem, J.-P. (2023) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntad172 · Cited by 13 · see citing works
Self-citationTwitter Misinformation Discourses About Vaping: Systematic Content Analysis
Al‐Rawi, A., Blackwell, B., Zemenchik, K., & Lee, K. (2023) · Journal of Medical Internet Research · doi:10.2196/49416 · Cited by 13 · see citing works
Sidani, J. E., Hoffman, B. L., Colditz, J. B., Wolynn, R., Hsiao, L., Chu, K., Rose, J. J., Shensa, A. S., Davis, E. M., & Primack, B. A. (2022) · JMIR Formative Research · doi:10.2196/26335 · Cited by 11 · see citing works
Self-citationZhou, R., Tang, Q., Xie, Z., & Li, D. (2023) · JMIR Formative Research · doi:10.2196/42706 · Cited by 11 · see citing works
Lopes, F. C., da Costa, M. P., Fernandez-Lazaro, C. I., Lara-Abelenda, F. J., Pereira-Sánchez, V., Teo, A. R., & Álvarez-Mon, M. Á. (2024) · BMC Public Health · doi:10.1186/s12889-023-17617-0 · Cited by 11 · see citing works
Gibson, L., Siegel, L. N., Kranzler, E. C., Volinsky, A. C., O’Donnell, M. B., Williams, S., Yang, Q., Kim, Y., Binns, S., Tran, H., et al. (2019) · Journal of Health Communication · doi:10.1080/10810730.2019.1682724 · Cited by 11 · see citing works
Potential Impact of FDA Flavor Enforcement Policy on Vaping Behavior on Twitter
Xie, Z., Ruan, J., Jiang, Y., Zhang, B., Chen, T., Luo, J., & Li, D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191912836 · Cited by 10 · see citing works
Küçükali, H., & Erdoğan, M. S. (2024) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntae276 · Cited by 11 · see citing works
Use of twitter for health communication
Díaz-Campo, J., Cambronero-Saiz, B., & Dominguez, M. Á. C. (2023) · REVISTA ESPAÑOLA DE COMUNICACIÓN EN SALUD · doi:10.20318/recs.2023.7049 · Cited by 6 · see citing works
Hammond, A. S., Paul, M. J., Hobelmann, J. G., Koratana, A. R., Dredze, M. H., & Chisolm, M. S. (2018) · JMIR Mental Health · doi:10.2196/mental.9903 · Cited by 6 · see citing works
SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text
Pant, K., Yanamandra, V. H., Debnath, A., & Mamidi, R. (2019) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/d19-5524 · Cited by 4 · see citing works
Bunyan, A., Venuturupalli, S., & Reuter, K. (2020) · JMIR Research Protocols · doi:10.2196/15716 · Cited by 4 · see citing works
Towards Sentiment Analysis of Tobacco Products’ Usage in Social Media
Yanamandra, V. H., Pant, K., & Mamidi, R. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_173 · Cited by 2 · see citing works
Lu, J., & Lee, E. W. J. (2021) · Journal of Medical Internet Research · doi:10.2196/28042 · Cited by 1 · see citing works
The Marketing and Perceptions of Non-Tobacco Blunt Wraps on Twitter
Rhee, J. U., Huang, Y., Soroosh, A. J., Alsudais, S., Ni, S., Kumar, A., Paredes, J., Li, C., & Timberlake, D. S. (2023) · Substance Use & Misuse · doi:10.1080/10826084.2023.2280572 · Cited by 1 · see citing works
Kelley, D., Brown, M., Murray, A., & Blake, K. D. (2019) · JMIR Public Health and Surveillance · doi:10.2196/12878 · Cited by 1 · see citing works
Bridging Tobacco Control Advocacy and Strategic Communication Scholarship
Jun, J., Choi, C., & Kim, J. (2021) · Wiley eBooks · doi:10.1002/9781118857205.ch22 · Cited by 1 · see citing works
Mowery, D. L., Park, A., & Conway, M. (2018) · viXra
Wu, D., Kasson, E. M., Singh, A. K., Ren, Y., Kaiser, N., Huang, M. S., & Cavazos‐Rehg, P. A. (2022) · doi:10.2196/preprints.39460
The social media response to the rollout of legalized cannabis retail in Ontario, Canada
Aversa, J. M., Jacobson, J., Hernández, T., Cleave, E., MacDonald, M., & Dizonno, S. (2024) · doi:10.32920/25475209
The social media response to the rollout of legalized cannabis retail in Ontario, Canada
Aversa, J. M., Jacobson, J., Hernández, T., Cleave, E., MacDonald, M., & Dizonno, S. (2024) · doi:10.32920/25475209.v1
Groznik, M., Jiménez, R. A., Tellesen, S., Kazzam, K., Anderson, S., Marium, S., & Record, R. A. (2025) · Online Information Review · doi:10.1108/oir-07-2024-0473
Cheng, N., Lam, M., Grové, C., & Wachowicz, M. (2026) · JMIR Mental Health · doi:10.2196/88731
Sadler, C. R., Grassby, T., Hart, K. H., Raats, M. M., Sokolović, M., & Timotijević, L. (2026) · Journal of Food Processing and Preservation · doi:10.1155/jfpp/9972623
- Authors
- Lienemann, B. A., Unger, J. B., Cruz, T. B., & Chu, K.-H.
- Affiliations
- University of Southern California (US)
- Corresponding author
- Lienemann, Brianna A
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2017-03-31
- Volume
- 19
- Issue
- 3
- Pages
- e91
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC5392207
- OpenAlex
- W2599314234
- Cited by
- 48
- References
- 36
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 4 · 2023: 6 · 2022: 8 · 2021: 8 · 2020: 6 · 2019: 6 · 2018: 7
- FWCI
- 0.64
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (T32CA009492, P50CA180905)
- Topics
- Health Sciences › Medicine › Physiology › Smoking Behavior and Cessation
- Social Sciences › Health › Social Media in Health Education
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- Twitter, electronic cigarettes, sentiment analysis, machine learning, social media surveillance, coding standards, user profiles, vulnerable populations
- MeSH terms
- Data Collection, Humans, Smoking, Social Marketing, Social Media, Tobacco Products, Electronic Nicotine Delivery Systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
