Misinformation is not about Bad Facts: An Analysis of the Production and Consumption of Fringe Content

TL;DR

This paper reframes misinformation as a problem of audience targeting and stylistic framing rather than a pure factual-accuracy issue, and analyzes how fringe groups repurpose legacy news to support their narratives. Using Australian news and fringe online data, it shows that information completeness is not strongly tied to publisher ideology, but that distinct writing styles emerge in production versus consumption, and across extreme groups. Through LIWC, Grievance, and StyloMetrix, the authors build style-based classifiers that can identify extremist content and separate production from consumption better than content-based baselines in some settings. The findings suggest misinformation interventions should address stylistic choices and audience framing, not just fact-checking, to effectively counter online fringe narratives.

Abstract

What if misinformation is not an information problem at all? To understand the role of news publishers in potentially unintentionally propagating misinformation, we examine how far-right and fringe online groups share and leverage established legacy news media articles to advance their narratives. Our findings suggest that online fringe ideologies spread through the use of content that is consensus-based and "factually correct". We found that Australian news publishers with both moderate and far-right political leanings contain comparable levels of information completeness and quality; and furthermore, that far-right Twitter users often share from moderate sources. However, a stark difference emerges when we consider two additional factors: 1) the narrow topic selection of articles by far-right users, suggesting that they cherry pick only news articles that engage with their preexisting worldviews and specific topics of concern, and 2) the difference between moderate and far-right publishers when we examine the writing style of their articles. Furthermore, we can identify users prone to sharing misinformation based on their communication style. These findings have important implications for countering online misinformation, as they highlight the powerful role that personal biases towards specific topics and publishers' writing styles have in amplifying fringe ideologies online.

Figures (4)

• Figure 1: Summary of the methods: we start by analysing contents of Australian news articles and sharing patterns of those articles in Twitter environment. Then we use linguistic styles -- not the actual article contents -- measured with LIWC, Grievance dictionary and StyloMetrix to identify extreme groups in Facebook. Next, we identify text styles employed by the extreme groups and classify them. Finally, we distinguish misinformation production and consumption using styles.
• Figure 2: Venn diagram showing the intersection of articles produced (FRProd, MDProd) and consumed (FRCons).
• Figure 3: Classification results of the moderate-produced articles (MDProd), far-right produced articles (FRProd) and the articles consumed by far-right users (FRCons). A Random Forest classifier and a Logistic Regression was used to report macro F1-score from 10-fold stratified cross-validation. The error bars represent 95% confidence intervals. We use 'lbfgs' solver with 'multi_class' option set to 'multinomial' to support three class classification for Logistic Regression.
• Figure 4: Confusion matrix of the three classes from Random Forest classifier using LGS features.