The QAnon Security Threat: a Linguistic Fusion-Based Violence Risk Assessment - Ebner, Julia (Perspectives on terrorism, 2022)
This study compares the narratives and language of QAnon groups in the encrypted messaging apps Telegram and Discord to those observed in the manifestos of terrorists. Drawing on our systematic linguistic analysis of fifteen terrorist manifestos that were published in the past decade, we developed a coding scheme which traces the narratives and linguistic markers that occur in the written communication of perpetrators of political violence. In this pilot study we apply our new coding scheme to QAnon content to assess the scale and nature of violence-associated narratives within the movement. Based on 200,000 messages that we collected from the online QAnon group "Great Awakening Community" on the gaming chat application Discord, we quantitatively examine to what degree they carry the trademarks of violent terrorist manifestos that are not found in non-violent texts. We then compared the results for the Great Awakening Community to content from both a non-violent and a violent-terrorist control group. To complement our computational assessment of QAnon narrative and linguistic patterns we share ethnographic observations from ten QAnon Telegram and Discord groups with English, German, and French speaking audiences. Past research has found that identity fusion in combination with a range of mediating and moderating variables is a strong predictor of violence in groups, and this is further supported by our terrorist manifesto analysis. Our study of QAnon messages found a high prevalence of linguistic identity fusion indicators along with external threat narratives, violence-condoning group norms as well as demonizing, dehumanizing, and derogatory vocabulary applied to the out-group, especially when compared to the non-violent control group. The aim of this piece of research is twofold: (i.) It seeks to evaluate the national security threat posed by the QAnon movement, and (ii.) it aims to provide a test of a novel linguistic toolkit aimed at helping to assess the risk of violence in online communication channels.