title: Predicting Retweeting Behavior on Breast Cancer Social Networks: Network and Content Characteristics.
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.
authors.affiliation: a Independent Scholar , Bogart , Georgia , USA.
authors.forename: Eunkyung
authors.initials: E
authors.lastname: Kim
authors.affiliation: b WE Communications , Seattle , Washington , USA.
authors.forename: Jiran
authors.initials: J
authors.lastname: Hou
authors.affiliation: c Department of Advertising and Public Relations , Henry W. Grady College of Journalism and Mass Communication, University of Georgia , Athens , Georgia , USA.
authors.forename: Jeong Yeob
authors.initials: JY
authors.lastname: Han
authors.affiliation: c Department of Advertising and Public Relations , Henry W. Grady College of Journalism and Mass Communication, University of Georgia , Athens , Georgia , USA.
authors.forename: Itai
authors.initials: I
authors.lastname: Himelboim
mesh_terms: D001943:Breast Neoplasms / Q000523:psychology*; D003142:Communication; D005260:Female; D006801:Humans; D016015:Logistic Models; D061108:Social Media / Q000706:statistics & numerical data*; D012944:Social Support