Digital Streets of Rage: Identifying Rhizomatic Extremist Messages During a Hybrid Media Event using Natural Language Processing

Abstract

This research explores how to identify extreme messages during a hybrid media event happening in a small language area by utilizing natural language processing (NLP), a type of artificial intelligence (AI). A hybrid media event gathers attention all sides of the media environment: mainstream media, social media, instant messaging apps and fringe communities. Hybrid media events call attention for participation and activities both in the physical world and online. On the darker side of media events, the media landscape can act as a channel for all kinds of disinformation, hate speech and conspiracy theories. In addition, fringe communities such as 4chan also spread hate speech and duplicated content during hybrid media events. From theoretical point of view, this connection between the physical world and information networks can be seen as rhizomatic in nature, because information spreads without regard to a traditional hierarchy. The result is that when individuals participate in a big media event, there is a viral awareness of different viewpoints and all kind of topics may be posted online for discussion. In addition, in rhizomatic context different kind of arguments can twist each other, “copy and paste”, and create very diversity meanings of new comments. The role of extremist speech in online spaces can have effects in physical world.

The focus of this paper is to present the findings of a case study on messages posted online by three different actor groups who participated in demonstrations organized on Finnish Independence Day. In this research, two data sets were collected from Twitter and Telegram and Natural Language Processing (NLP) was used to classify messages using extremist media index labels. Three actor groups were identified as participating in the demonstrations, and they were labelled as: far-right, antifascists and conspiracists. Computational analysis was done by using NLP to categorize the messages based upon the definitions provided by the extremist media index. The analysis shows how AI technology can help identifying messages which include extremist content and approve the use of violence in a small language area. The model of rhizome was valid in making the connections between fringe, extremist content and moderate discussion visible. This article is part of larger project related to extremist networks and criminality in online darknet environments.