Analysis and Prediction of Multilingual Controversy on Reddit

Social media users express their opinions about arbitrary subjects, including controversial matters such as the 2020 U.S. presidential election or climate change. Controversial topics typically attract user attention, which often lead to fruitful, but sometimes also heated discussions potentially segregating the community. Understanding features that are predictive of controversy in social media can improve moderation of communities and therefore the public discourse. In this paper, we analyze and predict controversy on the multilingual social platform Reddit. In particular, we compare a large set of textual and user activity features in controversial and non-controversial comments posted in six different languages. Using these features we perform a prediction task and study their predictive strengths for controversy. Our results indicate that, regardless of the language, controversial comments are harder to read, more negative and users follow up faster and more frequently to such comments. Moreover, with our prediction experiment (ROC AUC = 0.79) we find that across all languages user activity is the most predictive of controversy on Reddit. Our results contribute to an improved understanding of controversy in social media and can serve as a foundation for tools and models to automatically detect controversial content posted on such platforms.