FASCIST-O-METER: Classifier for Neo-fascist Discourse Online

2025-06-12

AI Summary

The rapid proliferation of neo-fascist discourse in U.S. digital spaces poses escalating threats to democratic institutions and the safety of marginalized communities. Method: This study introduces the first neo-fascist discourse identification system tailored to the U.S. sociopolitical context. We propose a political-science–informed coding scheme for neo-fascist rhetoric, trained on texts from Iron March and Stormfront forums alongside 1,000 expert-annotated samples. The system integrates fine-tuned RoBERTa and LLaMA models to enable computationally tractable detection. Contributions/Results: (1) We release the first structured, theory-grounded coding framework for neo-fascist speech; (2) we achieve high-precision automated classification of such extremist content (F1 > 0.85); and (3) we publicly release the annotated dataset and reproducible models, underscoring the critical role of sociocultural context in NLP modeling. This work provides both methodological foundations and deployable tools for real-time online risk monitoring in democratic societies.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Neuro-Symbolic Learning
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems

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 that are labeled as neo-fascist or non-neo-fascist. With this labeled data set, we fine-tune and test both Small Language Models (SLMs) and Large Language Models (LLMs), obtaining the very first classification models for neo-fascist discourse. We find that the prevalence of neo-fascist rhetoric in this kind of forum is ever-present, making them a good target for future research. The societal context is a key consideration for neo-fascist speech when conducting NLP research. Finally, the work against this kind of political movement must be pressed upon and continued for the well-being of a democratic society. Disclaimer: This study focuses on detecting neo-fascist content in text, similar to other hate speech analyses, without labeling individuals or organizations.

Problem

• Detect neo-fascist discourse in online forums using NLP.
• Develop a classification model for neo-fascist speech analysis.
• Address societal impact of neo-fascist rhetoric on democracy.

Innovation

• First neo-fascist coding scheme for digital discourse
• Fine-tune SLMs and LLMs for classification
• Crowdsourced annotation of neo-fascist posts