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Capturing social media signals for health research


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Presented at Digital Demography workshop at Max-Planck Institute for Demographic Research on Oct 17, 2018

Published in: Data & Analytics
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Capturing social media signals for health research

  1. 1. capturing social media signals for health research Yelena Mejova Previously:
  2. 2. 2 Time & date Location Photo Explanation Social response Ramadan Food Exercise Sentiment Wearable Phone
  3. 3. 3 Annie S. Anderson, (1995) British Food Journal, Vol. 97 Issue: 7, pp.22-26
  4. 4. 4 1995 Newsgroups 2000 Websites 2005 Social Media 2008 Smartphones 2013 Wearables 2017 Drones, VR, Genomics…
  5. 5. self-motivated plentiful real-time geo-located media rich social cultural interactive 5 self-image noisy bursty geo-biased complex signal influence contextual persuasive
  6. 6. Text Images Location Social Wearables Search Advertising 6
  7. 7. Text (Blogs, Microblogs, Comments, News…) • Track influenza-like-illness (ILI) symptoms • Global Epidemic and Mobility Model (GLEAM) • Predict Influenza seasons 7Zhang+ WWW’17 *ISI GPS Located
  8. 8. Text (Blogs, Microblogs, Comments, News…) • Food-related queries to Instagram • Relating foods in tags to “food deserts” 8De Choudhury+ CSCW’16 nutritional attributes of food deserts and non-food deserts
  9. 9. Text (Blogs, Microblogs, Comments, News…) • receiving social support in the first post leads to a relative increase in the achieved weight loss of 26%, or an absolute mean difference of 9 lbs. 9 loss age/gender height starting weight current weight goal weight Cunha+ WWW’17 *QCRI
  10. 10. Images (Flickr, Instagram…) 10 Garimella+ CHI’16 *QCRI • using labels extracted from images allows for better tracking of certain conditionsPearson’s r for predicting health statistics across 100 counties (U)ser tags, (I)magga tags, (D)emographics
  11. 11. Images (Flickr, Instagram…) 11Kocabey+ ICWSM’17 *QCRI BMI (Body Mass Index) • using deep learning on visual features works nearly as well as human labeling
  12. 12. Location (metadata) 12Mejova+ DH’15 • are check-ins at fast food restaurants associated with higher risk of obesity? • is there different behavior between low and high obesity areas?
  13. 13. • hybrid model of human mobility integrating Flickr data with the classical gravity model 13Beiró+ EPJ Data Science’16 *ISI Location (metadata)
  14. 14. Social (friendships, likes, comments…) 14Mejova+ ICWSM’17 • social perception of #foodporn, response to different framing #foodporn
  15. 15. Social (friendships, likes, comments…) 15Aral+Nicolaides Nature Communications’17 • social network for runners • measuring influence of runners on their friends’ running performance • using weather as instrumental variable to “randomize” data
  16. 16. Wearables (shared on SM) 16Wang+ DH’16, Akbar+ ICHI’16 *QCRI • building models to predict weight • comparing sleeping patterns across the world
  17. 17. Wearables (+ Games!) 17Althoff+ JMIR’16 • detect Pokemon Go users through “experiential queries” • link to Microsoft Band pedometer info • users issuing at least 10 queries (very interested in game) added 1473 daily steps in first week
  18. 18. Search Queries (ok, not quite SM) 18 Google Flu Trends Ginsberg+ Nature’09, Nuti+, Ojala+ ICWSM’17, PLOS’14, Yom-Tov+ JMIR’14 Bing for Tracking Mood Disorders Google Trends in 2014: infectious disease (27% of articles), mental health and substance use (24%), other non-communicable diseases (16%), and general population behavior (33%). By use, 27% of articles utilized Google Trends for casual inference, 39% for description, and 34% for surveillance. Google Correlate for Fertility
  19. 19. Advertisement (on SM platforms) see talks by Ingmar Weber & Ridhi Kashyap 19
  20. 20. 360o Epidemiology Tracking disease and its awareness in demographic, cultural, social, … context 20 Modeling health behavior Social influence Media influence Technology influence Campaigns and interventions Connecting with health orgs Building tools with population and individual data Data-driven intervention
  21. 21. • Zhang, Q., Perra, N., Perrotta, D., Tizzoni, M., Paolotti, D., & Vespignani, A. (2017). Forecasting seasonal influenza fusing digital indicators and a mechanistic disease model. International Conference on World Wide Web. web • De Choudhury, Munmun, Sanket Sharma, and Emre Kiciman. "Characterizing dietary choices, nutrition, and language in food deserts via social media." Proceedings of the 19th acm conference on computer-supported cooperative work & social computing. ACM, 2016. web • Cunha, Tiago, Ingmar Weber, and Gisele Pappa. "A warm welcome matters!: The link between social feedback and weight loss in/r/loseit." Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, 2017. web • Garimella, Venkata Rama Kiran, Abdulrahman Alfayad, and Ingmar Weber. "Social media image analysis for public health." Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2016. web • Kocabey, Enes, et al. "Face-to-BMI: using computer vision to infer body mass index on social media." ICWSM (2017). web • Mejova, Y., Haddadi, H., Noulas, A., & Weber, I. (2015, May). # foodporn: Obesity patterns in culinary interactions. In Proceedings of the 5th International Conference on Digital Health 2015 (pp. 51-58). ACM. web • Beiró, M. G., Panisson, A., Tizzoni, M., & Cattuto, C. (2016). Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Science, 5(1), 30. web • Mejova, Y., Abbar, S., & Haddadi, H. (2016, May). Fetishizing Food in Digital Age:# foodporn Around the World. In ICWSM. web • Aral, Sinan, and Christos Nicolaides. "Exercise contagion in a global social network." Nature communications 8 (2017): 14753. web • Wang, Y., Weber, I., & Mitra, P. (2016, April). Quantified self meets social media: sharing of weight updates on Twitter. In Proceedings of the 6th International Conference on Digital Health Conference (pp. 93-97). ACM. web • Akbar, F., & Weber, I. (2016, October). # Sleep_as_Android: Feasibility of Using Sleep Logs on Twitter for Sleep Studies. In Healthcare Informatics (ICHI), 2016 IEEE International Conference on (pp. 227-233). IEEE. web • Althoff, T., White, R. W., & Horvitz, E. (2016). Influence of Pokémon Go on physical activity: study and implications. Journal of medical Internet research, 18(12). web • Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012. web • Yom-Tov, E., White, R. W., & Horvitz, E. (2014). Seeking insights about cycling mood disorders via anonymized search logs. Journal of medical Internet research, 16(2). web • Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The use of google trends in health care research: a systematic review. PloS one, 9(10), e109583. web • Ojala, J., Zagheni, E., Billari, F. C., & Weber, I. (2017). Fertility and its meaning: Evidence from search behavior. ICWSM. web • Abbar, S., Mejova, Y., & Weber, I. (2015, April). You tweet what you eat: Studying food consumption through twitter. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3197-3206). ACM. web • Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England journal of medicine, 357(4), 370-379. web 21
  22. 22. 22 @yelenamejova * Previously