Collective victimhood narratives in far-right communities on Telegram

Abstract

ABSTRACT Feelings of collective victimhood have been demonstrated to have a strong effect on ingroup bias, outgroup hostility and support for violence. The use of narratives stirring these feelings in far-right communications is especially concerning given their inclusion in the manifestos of several mass killers across Europe and North America. However, scholars still have little knowledge on the reach of such narratives as well as the extent to which major salient events increase attention to collective victimhood messaging among far-right followers. To address these gaps, we analyze the use of collective victimhood narratives on the popular secure instant messaging service, Telegram, which has exploded in popularity in response to mainstream platforms’ attempts to moderate extremist speech. We develop a supervised machine learning algorithm to predict the presence of these discourses in text from over 18.5 million messages that were extracted from 1,870 far-right Telegram channels. We then use these data to test what impact the George Floyd protests and the storming of the US Capitol had on the frequency of collective narrative discussions on far-right Telegram. Our findings suggest that both events coincided with a significant increase in the use of victimhood narratives, thus providing insight into the radicalization process of far-right communities online.