The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
You Are What You Tweet: Analyzing Twitter for Public Health
Open accessAbstract
Analyzing user messages in social media can measure different population characteristics, including public health measures. For example, recent work has correlated Twitter messages with influenza rates in the United States; but this has largely been the extent of mining Twitter for public health. In this work, we consider a broader range of public health applications for Twitter. We apply the recently introduced Ailment Topic Aspect Model to over one and a half million health related tweets and discover mentions of over a dozen ailments, including allergies, obesity and insomnia. We introduce extensions to incorporate prior knowledge into this model and apply it to several tasks: tracking illnesses over times (syndromic surveillance), measuring behavioral risk factors, localizing illnesses by geographic region, and analyzing symptoms and medication usage. We show quantitative correlations with public health data and qualitative evaluations of model output. Our results suggest that Twitter has broad applicability for public health research.
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Eysenbach, G. (2009) · Journal of Medical Internet Research · doi:10.2196/jmir.1157 · Cited by 1,351
Hawn, C. (2009) · Health Affairs · doi:10.1377/hlthaff.28.2.361 · Cited by 821
Information contagion: an empirical study of the spread of news on digg and twitter social networks
Lerman, K., & Ghosh, R. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14021 · Cited by 813
Greene, J. A., Choudhry, N. K., Kilabuk, E., & Shrank, W. H. (2011) · Journal of General Internal Medicine · doi:10.1007/s11606-010-1526-3 · Cited by 777
Practicing Medicine in the Age of Facebook
Jain, S. H. (2009) · New England Journal of Medicine · doi:10.1056/nejmp0901277 · Cited by 179
Review of Extracting Information From the Social Web for Health Personalization
Fernandez-Luque, L., Karlsen, R., & Bonander, J. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1432 · Cited by 61
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Statistical physics of vaccination
Wang, Z., Bauch, C. T., Bhattacharyya, S., d'Onofrio, A., Manfredi, P., Perc, M., Perra, N., Salathé, M., & Zhao, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.006 · Cited by 872
Mental Health Discourse on reddit: Self-Disclosure, Social Support, and Anonymity
De Choudhury, M., & De, S. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14526 · Cited by 836
Quantifying Mental Health Signals in Twitter
Coppersmith, G., Dredze, M., & Harman, C. (2014) · Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality · doi:10.3115/v1/w14-3207 · Cited by 722
Self-citationBig data for development: a review of promises and challenges
Hilbert, M. (2016) · Development Policy Review · doi:10.1111/dpr.12142 · Cited by 679
Social media as a measurement tool of depression in populations
De Choudhury, M., Counts, S., & Horvitz, E. (2013) · Proceedings of the 5th Annual ACM Web Science Conference · doi:10.1145/2464464.2464480 · Cited by 497
Metadata
- Authors
- Paul, M., & Dredze, M.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 5
- Issue
- 1
- Pages
- 265-272
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W201361503
- Cited by
- 947
- References
- 6
- FWCI
- 184.57
- Type
- conference paper
- Language
- en
- Open access
- diamond
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