Appraisal Patterns as Predictors of Emotional Expressions And Shares on Political Social Networking Sites

Abstract

Emotions are considered important drivers of the diffusion of messages on social networking sites. Therefore, emotion-eliciting political communication yields the potential to reach broad audiences and to influence citizens’ attitudes and behavior. In this study, we investigate message characteristics that potentially trigger emotional reactions on part of the users of political social networking pages and test if this fosters the diffusion of political content in the network. Based on appraisal theory, we employ a manual coding scheme to identify appraisal dimensions in political parties’ Facebook posts that should trigger sadness or anger. We subsequently combine the manual codings with information of the users’ reactions to the respective posts, which we gathered using an automated content analysis. More specifically, we determine (1) if posts that include sadness or anger appraisals are associated with the corresponding emotional reactions in the form of emojis and (2) if these posts are shared more often.