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Détection de messages abusifs au moyen de réseaux conversationnels

Abstract : While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance, with results comparable to those previously obtained with a content-based approach. MOTS-CLÉS : Catégorisation de texte, Détection d'abus, Communautés en ligne, Modération
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Contributor : Vincent Labatut Connect in order to contact the contributor
Submitted on : Tuesday, October 10, 2017 - 4:26:29 PM
Last modification on : Friday, November 12, 2021 - 11:18:05 AM


Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License


  • HAL Id : hal-01614279, version 1



Etienne Papegnies, Richard Dufour, Vincent Labatut, Georges Linarès. Détection de messages abusifs au moyen de réseaux conversationnels. 8ème Conférence Modèles et Analyse des Réseaux : Approches Mathématiques et Informatiques (MARAMI), Oct 2017, La Rochelle, France. ⟨hal-01614279⟩



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