Social Media

Social media is an exciting new space to learn about health behaviors and attitudes in ways that avoid the response bias in traditional research methods. Our team engages in novel data mining, natural language processing, and network dynamics methodologies to understand how individuals and populations communicate about health on social media. In addition to self-reporting behaviors and attitudes, social media allows for the development of virtual communities that allows for individuals who identify with particular behaviors, attitudes and sentiments to discuss topics of interest, which can then be identified for researchers studying these groups. For hard-to-reach populations, social media is particularly useful in filling in gaps in the research regarding health behaviors and attitudes that may carry stigma or are understudied more generally. To compliment our research activities, we are also at the cutting-edge of using social media to positively influence health behaviors online.

Our team has leveraged social media data to study an array of topics, including: cervical cancer screening, opioid misuse and cancer survivorship.

word-cloud from study representing lexicon of codeine misuse on Instagram

Funding sources

National Cancer Institute (NCI) R01 grant: Social Networks Online Working as a Behavioral and Learning Laboratory (SNOWBALL)

Selected Publications

  1. Lyson HC, Le GM, Zhang J, Rivadeneira N, Lyles C, Radcliffe K, Pasick RJ, Sawaya G, Sarkar U, Centola D. Social Media as a Tool to Promote Health Awareness: Results from an Online Cervical Cancer Prevention Study. J Cancer Educ. 2018 Jun 11. PMID: 29948924.

  2. Sarkar U, Le GM, Lyles CR, Ramo D, Linos E, Bibbins-Domingo K. Using Social Media to Target Cancer Prevention in Young Adults: Viewpoint. J Med Internet Res. 2018 Jun 05; 20(6):e203. PMID: 29871850.

  3. Cherian R, Westbrook M, Ramo D, Sarkar U. Representations of Codeine Misuse on Instagram: Content Analysis. JMIR Public Health Surveill. 2018 Mar 20; 4(1):e22. PMID: 29559422.

  4. Lyles CR, Godbehere A, Le G, El Ghaoui L, Sarkar U. Applying Sparse Machine Learning Methods to Twitter: Analysis of the 2012 Change in Pap Smear Guidelines. A Sequential Mixed-Methods Study. JMIR Public Health Surveill. 2016 Jun 10; 2(1):e21. PMID: 27288093; PMCID: PMC4920957.

  5. Giardina TD, Sarkar U, Gourley G, Modi V, Meyer AN, Singh H. Online public reactions to frequency of diagnostic errors in US outpatient care. Diagnosis (Berl). 2016 03; 3(1):17-22. PMID: 27347474.

  6. Chan B, Lopez A, Sarkar U. The Canary in the Coal Mine Tweets: Social Media Reveals Public Perceptions of Non-Medical Use of Opioids. PLoS One. 2015; 10(8):e0135072. PMID: 26252774; PMCID: PMC4529203.

  7. Wallace BC, Paul MJ, Sarkar U, Trikalinos TA, Dredze M. A large-scale quantitative analysis of latent factors and sentiment in online doctor reviews. J Am Med Inform Assoc. 2014 Nov-Dec; 21(6):1098-103. PMID: 24918109; PMCID: PMC4215053.

  8. Detz A, López A, Sarkar U. Long-term doctor-patient relationships: patient perspective from online reviews. J Med Internet Res. 2013 Jul 02; 15(7):e131. PMID: 23819959; PMCID: PMC3713916.

  9. Lyles CR, López A, Pasick R, Sarkar U. "5 mins of uncomfyness is better than dealing with cancer 4 a lifetime": an exploratory qualitative analysis of cervical and breast cancer screening dialogue on Twitter. J Cancer Educ. 2013 Mar; 28(1):127-33. PMID: 23132231.

  10. López A, Detz A, Ratanawongsa N, Sarkar U. What patients say about their doctors online: a qualitative content analysis. J Gen Intern Med. 2012 Jun; 27(6):685-92. PMID: 22215270; PMCID: PMC3358396.

Creative Commons License
This work by Urmimala Sarkar is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License .
Based on a work at sarkarlab.ucsf.edu.