Health Information and the Like—Response

Abstract

Reddi's thoughtful comments highlight the importance to global health policy of understanding the spread of health behaviors in society. Social media sites and online communication platforms are becoming increasingly popular sources for the dissemination of health information about prevention, medical conditions, and treatments. They are therefore central to the promulgation of beliefs about health threats and the efficacy of health interventions. To explain the critical points of divergence between our results and Reddi's conclusions, we must critically evaluate the scientific evidence. First, ours is a randomized experiment, whereas the studies to which Reddi refers rely on observational data to make claims about information diffusion (the authors of those studies are careful to note that their evidence is predictive rather than causal). Our approach, which randomly manipulates the ratings of online content, holds author and content effects constant, isolating the causal effect of upvoting or downvoting on opinion and behavior change. For example, the studies Reddi refers to analyze whether negative or positive content about vaccinations is more likely to be retweeted. They do not exclude several likely alternative explanations of retweeting behavior that may have nothing to do with whether negative tweets are actually changing people's opinions about vaccinations. The majority of Twitter users may already have a negative opinion of vaccinations, inspiring more retweets of such content. The evidence Reddi cites shows that negative information spreads farther and faster. Our experiment shows that negative information is less likely to change people's opinions than positive information. These two claims are not contradictory. In fact, they are complementary pieces of evidence in a broader story of information diffusion and behavior change. Observational data about and simulations of information diffusion are useful for understanding how information and awareness spread ([ 1 ][1], [ 2 ][2]), but causal inference is necessary to understand how information diffusion subsequently changes opinions and behavior ([ 3 ][3]–[ 5 ][4]). Combining causal analysis of behavior change with predictive analysis of information diffusion creates a powerful lens through which to understand the ebbs and flows of behaviors on our planet. Second, analogies between ratings systems and Twitter will always remain loose at best. Collective opinion rating aggregates and presents the opinion of the community about specific content. Voters are likely motivated by the hope that their vote may influence the opinion of other readers. (Re)tweeting information on Twitter is also likely motivated by the desire to maximize publicity for certain information, the desire to become known as a curator of certain information, and the desire for personal exposure. Ratings are anonymous and do not push information to one's “followers,” making these motivations less likely in ratings systems than on Twitter. The influence mechanisms in these scenarios differ as well. Twitter users are not privy to collective opinion on a topic when they tweet about it, whereas they are precisely aware of collective opinion when they vote on or rate an item. Thus, the motivations for diffusing information and rating it likely differ. Perhaps more important, differences in the designs of these systems could drive differences between our results and results from studies of Twitter. Whereas the system we studied enables users to either upvote, downvote, or abstain, there is no feature analogous to the downvote on Twitter. The user can only choose to either retweet or not. Facebook operates similarly: One can like content, but not dislike it. Our results generalize to collective opinion aggregation and ratings systems; results from studies of Twitter likely generalize to microblogging and networked information dissemination systems. 1. [↵][5] 1. E. Adar, 2. L. A. Adamic , in The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (Compiegne, France, 2005), pp. 207–214. 2. [↵][6] 1. S. Aral, 2. M. Van Alstyne , Am. J. Soc. 117, 90 (2011). [OpenUrl][7][CrossRef][8] 3. [↵][9] 1. S. Aral, 2. L. Muchnik, 3. A. Sundararajan , Proc. Natl. Acad. Sci. 106, 21544 (2009). [OpenUrl][10][Abstract/FREE Full Text][11] 4. 1. C. R. Shalizi, 2. A. C. Thomas , Soc. Meth. Res. 40, 211 (2011). [OpenUrl][12][CrossRef][13] 5. [↵][14] 1. S. Aral, 2. L. Muchnik, 3. A. Sundararajan , Network Sci. 1, (2013). [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-5 [5]: #xref-ref-1-1 View reference 1 in text [6]: #xref-ref-2-1 View reference 2 in text [7]: {openurl}?query=rft.jtitle%253DAm.%2BJ.%2BSoc.%26rft.volume%253D117%26rft.spage%253D90%26rft_id%253Dinfo%253Adoi%252F10.1086%252F661238%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [8]: /lookup/external-ref?access_num=10.1086/661238&link_type=DOI [9]: #xref-ref-3-1 View reference 3 in text [10]: {openurl}?query=rft.jtitle%253DProc.%2BNatl.%2BAcad.%2BSci.%26rft_id%253Dinfo%253Adoi%252F10.1073%252Fpnas.0908800106%26rft_id%253Dinfo%253Apmid%252F20007780%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [11]: /lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NDoicG5hcyI7czo1OiJyZXNpZCI7czoxMjoiMTA2LzUxLzIxNTQ0IjtzOjQ6ImF0b20iO3M6MjU6Ii9zY2kvMzQyLzYxNjQvMTMxNS4yLmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ== [12]: {openurl}?query=rft.jtitle%253DSoc.%2BMeth.%2BRes.%26rft.volume%253D40%26rft.spage%253D211%26rft_id%253Dinfo%253Adoi%252F10.1177%252F0049124111404820%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [13]: /lookup/external-ref?access_num=10.1177/0049124111404820&link_type=DOI [14]: #xref-ref-5-1 View reference 5 in text