"You wouldn't celebrate September 11": Testing online polarisation between opposing ideological camps on YouTube. (September 2020)

• Record Type: Journal Article
• Authors: Bliuc, Ana-Maria; Smith, Laura G. E.; Moynihan, Tina
• Other Names: Hopthrow Tim guest-editor.; Smith Laura G. E. guest-editor.; Levine Mark guest-editor.
• Is Part Of: Group processes and intergroup relations. Volume 23:Number 6(2020)
• Page Start: 827
• Page End: 844
• Publication Date: 2020-09
• Subjects: computerised linguistic analysis -- ideological polarisation -- online interaction -- social identity; Intergroup relations -- Periodicals; Social groups -- Periodicals; 302.305
• DOI: 10.1177/1368430220942567
• Languages: English
• ISSNs: 1368-4302

Abstract

Online communication is increasingly associated with growing polarisation in society. In this research, we test a dual-pathway model of online polarisation via intergroup and intragroup interaction of supporters of opposing ideological camps on YouTube. The interaction occurs over a video parody promoting a campaign to change the date of Australia Day celebrations, a divisive issue entailing contrasting narratives about Australian identity, meanings of Australia Day, and interpretations of colonial history. To capture ideological polarisation, we conducted computerised linguistic analysis of polarised talk in the form of comments and replies ( N = 1, 027) from supporters and opponents of the campaign. The indicators used to capture polarisation are social identification, position certainty, and psychological distance (as reflected by increased anxiety and hostility). Our results show that most polarisation (in the form of increased hostility) occurs in conditions of expression of outgroup dissent (the intergroup interaction pathway) and the most debated content on the online forum revolves around themes relevant to group identity. In addition to contributing to the understanding of group process in an online context, another key contribution of this research is providing a theory-driven method and blueprint to detect polarisation in social media data.