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CUET_Novice@DravidianLangTech 2025: Abusive Comment Detection in Malayalam Text Targeting Women on Social Media Using Transformer-Based Models

Domaine:

natural language processing

Type de record:

paper
Créateur:
AssDeyOsaSay
Éditeur:
Und
Hôte:avatar
Social media has become a widely used platform for communication and entertainment, but it has also become a space where abuse and harassment can thrive. Women, in particular, face hateful and abusive comments that reflect gender inequality. This paper discusses our participation in the Abusive Text Targeting Women in Dravidian Languages shared task at DravidianLangTech@NAACL 2025, which focuses on detecting abusive text targeting women in Malayalam social media comments. The shared task provided a dataset of YouTube comments in Tamil and Malayalam, focusing on sensitive and controversial topics where abusive behavior is prevalent. Our participation focused on the Malayalam dataset, where the goal was to classify comments into these categories accurately. Malayalam-BERT achieved the best performance on the subtask, securing 3rd place with a macro f1-score of 0.7083, highlighting the effectiveness of transformer models for low-resource languages. These results contribute to tackling gender-based abuse and improving online content moderation for underrepresented languages.

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doi.orgunderline.io

Tasks

hate speech detectiontext classification

Tags

Artificial IntelligenceComputational LinguisticsNatural Language Processing

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