MALTO at FadeIT: A BERT-Based System for Multi-Label Fallacy Detection in Italian Social Media
Year: 2026
Type: conference-paper
Abstract: Fallacies in social media posts are common and can make texts less trustworthy, and more likely to foster biased views. In this work, we study multi-label fallacy classification in Italian social media posts by detecting 20 different fallacy types that can appear in the same post. We address this task using encoder-based language models. In particular, we build a fallacy classifier based on AlBERTo, a BERT-style encoder for Italian, and use a global multi-label threshold to turn the model’s outputs into the final set of fallacy labels. Our results indicate that paraphrase-based augmentation can degrade performance in fallacy detection, likely by introducing label noise, while the choice of loss function substantially affects the trade-off between micro- and macro-averaged performance
Source: PORTO@Iris
Institution: Politecnico di Torino
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7216877390