Predicting Retweet Behavior in Breast Cancer Social Networks
Kim, E, Hou, J., Han, J.Y., & Himelboim, I. (2016). Predicting Retweet Behavior in Breast Cancer Social Networks: Network and Content Characteristics. Journal of Health Communication, 21(4), 479-486.
Abstract: This study explored how social media, especially Twitter, serves as a viable place for communicating about cancer. Using a 2-step analytic method that combined social network analysis and computer-aided content analysis, this study investigated (a) how different types of network structures explain retweeting behavior and (b) which types of tweets are retweeted and why some messages generate more interaction among users. The analysis revealed that messages written by users who had a higher number of followers, a higher level of personal influence over the interaction, and closer relationships and similarities with other users were retweeted. In addition, a tweet with a higher level of positive emotion was more likely to be retweeted, whereas a tweet with a higher level of tentative words was less likely to be retweeted. These findings imply that Twitter can be an effective tool for the dissemination of health information. Theoretical and practical implications for psychosocial interventions for people with health concerns are discussed.
Abstract: Participants (N=88) in a two-condition (Facebook post information level: high vs. low) mixed factorial design took part in a laboratory experiment that utilized eye tracking to gauge what areas of the page in common news layouts attract viewers’ gaze, and whether this viewing amount of information about the story disclosed in the Facebook posts. […]
Bartosz WojdynskiCamila EspinaKate KeibJennifer MalsonHyejin BangYen-I Lee
A Social Networks Approach to Online Social Movement:
Abstract: The movement to free Al Jazeera journalists (#FreeAJStaff), imprisoned by Egyptian authorities, utilized Twitter over almost two years, between 2014 and 2015. This study applied a social networks approach to study patterns of information flow, social mediators, and clusters, formed by the #FreeAJStaff movement on Twitter.Analysis of 22 months of data found social mediators […]