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Akatsuki-CIOL@DravidianLangTech 2025: Ensemble-Based Approach Using Pre-Trained Models for Fake News Detection in Dravidian Languages

Domaine:

natural language processing

Type de record:

modelpaper
Créateur:
AssAhsAniFai
Éditeur:
Und
Hôte:avatar
The widespread spread of fake news on social media poses significant challenges, particularly for low-resource languages like Malayalam. The accessibility of social platforms accelerates misinformation, leading to societal polarization and poor decision-making. Detecting fake news in Malayalam is complex due to its linguistic diversity, code-mixing, and dialectal variations, compounded by the lack of large labeled datasets and tailored models. To address these, we developed a fine-tuned transformer-based model for binary and multiclass fake news detection. The binary classifier achieved a macro F1 score of 0.814, while the multiclass model, using multimodal embeddings, achieved a score of 0.1978. Our system ranked 14th and 11th in the shared task competition, highlighting the need for specialized techniques in underrepresented languages. Our full experimental codebase is publicly available at: ciol-researchlab/NAACL25-Akatsuki-Fake-News-Detection.

Visit

doi.orgunderline.io

Tasks

text classification

Tags

Artificial IntelligenceComputational LinguisticsNatural Language Processing

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