Is Constructive Engagement Online a Lost Cause? Toxic Outrage in Online User Comments Across Democratic Political Systems and Discussion Arenas

Abstract

This study is the first to simultaneously investigate country-level and platform-related context factors of toxic outrage, that is, destructive incivility, in online discussions. It compares user comments on the public role of religion and secularism from 2015/16 in four democracies (Australia, United States, Germany, Switzerland) and four discussion arenas on three platforms (News websites, Facebook, Twitter). A novel automated content analysis ( N = 1,236,551) combines LIWC dictionaries with machine learning. The level of toxic outrage is higher in majoritarian than in consensus-oriented democracies and in arenas that afford plural, issue-driven rather than like-minded, preference-driven debates. Yet, toxic outrage is lower in forums that tend to separate public and private conversations than in those that collapse varying contexts. This suggests that user-generated discussions flourish in environments that incentivize actors to strive for compromise, put relevant issues center stage and make room for public debate at a relative distance from purely social conversation.

Comparing and mapping difference indices of debate quality on Twitter

“…However, further dimensions could be included in the definition of debate quality. For instance, Jakob et al (2022) assessed how the political system of a country and the type of discussion arena (namely, blog posts, Facebook posts and tweets) condition toxic outrage online as a violation of civility norms. Debate quality could also be supplemented by the analysis of argument quality (e.g.…”

“…Concerning the realm of social media, Brundidge et al (2014) showed that conservative bloggers were less integrative than liberal bloggers, thus highlighting the risk of a potential exacerbation of divisions in cognitive-linguistic styles on polarised political blogs. On a contextual level, Jakob et al (2022) showed that toxic outrage depends on the type of democratic system , and that it is mainly higher in majoritarian democracies than in consensus-oriented democracies and in arenas that afford plural and issue-driven, rather than like-minded and preference-driven, debates. From the broader perspective of democratic deliberation, there are additional factors worth considering that can impact on the quality of political debate.…”

Cutting Through the Comment Chaos: A Supervised Machine Learning Approach to Identifying Relevant YouTube Comments

“…For instance, in the field of journalism studies, Stoll et al (2020) test and compare different SML techniques to predict impoliteness and incivility in online discussions on German news media outlets on Facebook. More recently, Jakob et al (2022) combined LIWC dictionaries with machine learning to study destructive incivility in online user comments on news websites, Facebook, and Twitter.…”

Distant Political News Classification: Facilitating Machine Learning Identification of News Topics Across Multilingual Text Corpora

“…Previous work has shown that computational tools and methods offer new opportunities to identify and analyze the vast size of news, varying from dictionary-based techniques, neural networks, to supervised machine learning (Makhortykh et al, 2022;DISTANT POLITICAL NEWS CLASSIFICATION 6 Trilling, Tolochko, & Burscher, 2017). While some scholars have used computational tools to predict news values (see e.g., Burggraaff & Trilling, 2020;Trilling et al, 2017), the prevalence of generic news frames (see e.g., Burscher, Odijk, Vliegenthart, De Rijke, & De Vreese, 2014;Kroon, van der Meer, & Vliegenthart, 2022;Opperhuizen, Schouten, & Klijn, 2019), the impoliteness and incivility in online user comments on news websites (see e.g., Jakob, Dobbrick, Freudenthaler, Haffner, & Wessler, 2022;Stoll, Ziegele, & Quiring, 2020), or media bias (see e.g., Budak, Goel, & Rao, 2016), the current study focuses on the automated classification of news topics.…”