1. Clinical informatics and Digital Health.My top five papers of 2015-2016 about informatics and the relationship between social media and health research G Bingham Clinical Informatics and Digital HealthCOM_01481

2. About Me • Identify yourself and your clinical profession • Describe the focus of your review • Why I chose it?

3. Search Strategy • A systematic review was conducted to identify publications that described social media use in relation to health research • In December 2016, searches of four bibliographic databases were conducted: Medline (Ovid), EMBASE (Ovid), Cochrane CENTRAL and CINAHL plus • Subsequent key word searching of titles and abstracts for social media-related terms (online, media, social, instagram, google, facebook, YouTube) was then conducted

4. Paper Selection • Selection of abstracts was undertaken by the reviewer (GB). • Inclusion criteria were primary clinical studies, with English full texts available, with a main subject of social media and health explicitly discussed.

5. Dunn, A. G., et al. (2015). "Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study." J Med Internet Res 17(6): e144. • Retrospective analysis of tweet content • Exploring relationship between exposure to negative information and subsequent negative expressions • Users more likely to express negative views if previous negative exposure • First study exploring vaccine related content • Further study on relationship between expressing negative views and subsequent decision making required

6. Zhou, X., et al. (2015). "Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter." Stud Health Technol Inform 216: 761-765. • Comparison study examining relationship of social connection versus content information from tweets about human papillomavirus (HPV) vaccines could be used to train classifiers to identify anti-vaccine opinions. • Machine learning methods were used to train classifiers using the first three months of data then tested over a following three months • Connection-based classifiers performed similarly to content-based classifiers on the first three months of training data, and performed more consistently than content-based classifiers on test data from the subsequent three months • Information about how people are connected, rather than what they write, may be useful for improving public health surveillance methods on Twitter.

7. Sinnenberg, L., et al. (2016). "Twitter as a Tool for Health Research: a Systematic Review." Am J Public Health: e1-e8. • Systematic Review for peer reviewed original research articles relating to Twitter use in health research. • The authors undertook their literature search through a variety of databases including; PubMEd, Embase, Web of Science, Google Scholar and CINAHL till September 2015. • Results summarised a taxonomy of use revealing Twitter was used in health care as follows; • for analysis (56%; n = 77) • surveillance (26%; n = 36) • engagement (14%; n = 19) • recruitment (7%; n = 9) • intervention (7%; n = 9) • network analysis (4%; n = 5).

8. Hamad, E. O., et al. (2016). "Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds." J Med Internet Res 18(3): e60. • Literature review focusing on content analysis of Twitter feeds and guidelines for health care professional researches on how to best analyse data • PubMed search to collect studies published between 2010 and 2014 that used Content Analysis to analysehealth care-related tweets • Results from 18 studies were a mix of quantitative and qualitative analyses • Proposed a combined content analysis framework for health researchers to use

9. Fung, I. C., et al. (2016). "Ebola virus disease and social media: A systematic review." Am J Infect Control. • Systematic review of social media use relating to Ebola virus disease • The authors undertook their literature search through a variety of databases including; PubMEd, Web of Science and EBSCO host till September 2015 finding 12 articles • Primary sources were; Twitter, Weibo, Facebook and YouTube • Themes or topics of social media contents, • Meta-data of social media posts (such as frequency of original posts and reposts, impression • Characteristics of the social media accounts that made these posts (such as whether they are individuals or institutions). • Ebola epidemic in 2014-15 demonstrated role for social media

10. Recommendations, predictions and questions for further research • Social media use is a growing field within healthcare • It is likely to become more prevalent in the future • The extent of social media influence on health related behaviour is unclear • There are emerging research opportunities to determine this

11. References • Dunn, A. G., Leask, J., Zhou, X., Mandl, K. D., & Coiera, E. (2015). Associations Between Exposure to and Expression of Negative Opinions About Human Papillomavirus Vaccines on Social Media: An Observational Study. J Med Internet Res, 17(6), e144. doi: 10.2196/jmir.4343 • Fung, I. C., Duke, C. H., Finch, K. C., Snook, K. R., Tseng, P. L., Hernandez, A. C., . . . Tse, Z. T. (2016). Ebola virus disease and social media: A systematic review. Am J Infect Control. doi: 10.1016/j.ajic.2016.05.011 • Hamad, E. O., Savundranayagam, M. Y., Holmes, J. D., Kinsella, E. A., & Johnson, A. M. (2016). Toward a Mixed-Methods Research Approach to Content Analysis in The Digital Age: The Combined Content-Analysis Model and its Applications to Health Care Twitter Feeds. J Med Internet Res, 18(3), e60. doi: 10.2196/jmir.5391 • Sinnenberg, L., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L., & Merchant, R. M. (2016). Twitter as a Tool for Health Research: a Systematic Review. Am J Public Health, e1-e8. doi: 10.2105/ajph.2016.303512 • Zhou, X., Coiera, E., Tsafnat, G., Arachi, D., Ong, M. S., & Dunn, A. G. (2015). Using social connection information to improve opinion mining: Identifying negative sentiment about HPV vaccines on Twitter. Stud Health Technol Inform, 216, 761-765.