A Comparison of Online Hate on Reddit and 4chan: A Case Study of the 2020 US Election

The rapid integration of the Internet into our daily lives has led to many benefits but also to a number of new, wide-spread threats such as online hate, trolling, bullying, and generally aggressive behaviours. While research has traditionally explored online hate, in particular, on one platform, the reality is that such hate is a phenomenon that often makes use of multiple online networks. In this article, we seek to advance the discussion into online hate by harnessing a comparative approach, where we make use of various Natural Language Processing (NLP) techniques to computationally analyse hateful content from Reddit and 4chan relating to the 2020 US Presidential Elections. Our findings show how content and posting activity can differ depending on the platform being used. Through this, we provide initial comparison into the platform-specific behaviours of online hate, and how different platforms can serve specific purposes. We further provide several avenues for future research utilising a cross-platform approach so as to gain a more comprehensive understanding of the global hate ecosystem.

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is three-fold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.