AIWizards at MULTIPRIDE: A Hierarchical Approach to Slur Reclamation Detection
Luca Tedeschini, Matteo Fasulo

TL;DR
This paper introduces a hierarchical approach to detect reclaimed slurs in hate speech, leveraging sociolinguistic cues and user identity modeling to improve detection accuracy in multilingual contexts.
Contribution
It proposes a novel two-stage hierarchical framework that incorporates sociolinguistic signals and user identity to enhance reclaimed slur detection in hate speech systems.
Findings
Achieves performance comparable to strong BERT baselines.
Provides a modular framework for sociolinguistic context integration.
Demonstrates effectiveness on Italian and Spanish datasets.
Abstract
Detecting reclaimed slurs represents a fundamental challenge for hate speech detection systems, as the same lexcal items can function either as abusive expressions or as in-group affirmations depending on social identity and context. In this work, we address Subtask B of the MultiPRIDE shared task at EVALITA 2026 by proposing a hierarchical approach to modeling the slur reclamation process. Our core assumption is that members of the LGBTQ+ community are more likely, on average, to employ certain slurs in a eclamatory manner. Based on this hypothesis, we decompose the task into two stages. First, using a weakly supervised LLM-based annotation, we assign fuzzy labels to users indicating the likelihood of belonging to the LGBTQ+ community, inferred from the tweet and the user bio. These soft labels are then used to train a BERT-like model to predict community membership, encouraging the…
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Taxonomy
TopicsHate Speech and Cyberbullying Detection · Spam and Phishing Detection · Sentiment Analysis and Opinion Mining
