FASCIST-O-METER: Classifier for Neo-fascist Discourse Online
Rudy Alexandro Garrido Veliz, Martin Semmann, Chris Biemann, Seid Muhie Yimam

TL;DR
This paper introduces the first NLP-based classifier for detecting neo-fascist discourse online, using a new coding scheme, annotated datasets, and fine-tuned language models to address the societal threat posed by such rhetoric.
Contribution
It develops a novel neo-fascist coding scheme, creates a labeled dataset from online forums, and trains the first classification models for this specific discourse.
Findings
Neo-fascist rhetoric is prevalent in online forums.
Language models can effectively classify neo-fascist content.
Societal context influences neo-fascist speech detection.
Abstract
Neo-fascism is a political and societal ideology that has been having remarkable growth in the last decade in the United States of America (USA), as well as in other Western societies. It poses a grave danger to democracy and the minorities it targets, and it requires active actions against it to avoid escalation. This work presents the first-of-its-kind neo-fascist coding scheme for digital discourse in the USA societal context, overseen by political science researchers. Our work bridges the gap between Natural Language Processing (NLP) and political science against this phenomena. Furthermore, to test the coding scheme, we collect a tremendous amount of activity on the internet from notable neo-fascist groups (the forums of Iron March and Stormfront.org), and the guidelines are applied to a subset of the collected posts. Through crowdsourcing, we annotate a total of a thousand posts…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Populism, Right-Wing Movements · Misinformation and Its Impacts
