Delighting and Detesting Engagement: Emotional Politics of Junk News

Laura Savolainen, Damian Trilling, Dimitra Liotsiou

Research output: Contribution to journalArticleScientificpeer-review

Abstract

How do audiences make sense of and interact with political junk news on Facebook? How does the platform's "emotional architecture" intervene in these sense-making, interactive processes? What kinds of mediated publics emerge on and through Facebook as a result? We study these questions through topic modeling 40,500 junk news articles, quantitatively analyzing their engagement metrics, and a qualitative comment analysis. This exploratory research design allows us to move between levels of public discourse, zooming in from cross-outlet talking points to microsociological processes of meaning-making, interaction, and emotional entrainment taking place within the comment boxes themselves. We propose the concepts of delighting and detesting engagement to illustrate how the interplay between audiences, platform architecture, and political junk news generates a bivalent emotional dynamic that routinely divides posts into highly "loved" and highly "angering." We argue that high-performing (or in everyday parlance, viral) junk news bring otherwise disparate audience members together and orient their dramatic focus toward objects of collective joy, anger, or concern. In this context, the nature of political junk news is performative as they become resources for emotional signaling and the construction of group identity and shared feeling on social media. The emotions that animate junk news audiences typically refer back to a transpiring social relationship between two political sides. This affectively loaded "us" versus "them" dynamic is both enforced by Facebook's emotional architecture and made use of by junk news publishers.

Original languageEnglish
Article number2056305120972037
JournalSocial Media + Society
Volume6
Issue number4
Pages (from-to)1-13
Number of pages13
ISSN2056-3051
DOIs
Publication statusPublished - Oct 2020
MoE publication typeA1 Journal article-refereed

Fields of Science

  • 512 Business and Management
  • Facebook
  • disinformation
  • algorithms
  • emotion
  • computational-qualitative

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