Multilingual Models for Check-Worthy Social Media Posts Detection

• URL: http://arxiv.org/abs/2408.06737v1
• Date: Tue, 13 Aug 2024 08:55:28 GMT
• Title: Multilingual Models for Check-Worthy Social Media Posts Detection
• Authors: Sebastian Kula, Michal Gregor,
• Abstract summary: The study includes a comprehensive analysis of different models, with a special focus on multilingual models. The novelty of this work lies in the development of multi-label multilingual classification models that can simultaneously detect harmful posts and posts that contain verifiable factual claims in an efficient way.
• Score: 0.552480439325792
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work presents an extensive study of transformer-based NLP models for detection of social media posts that contain verifiable factual claims and harmful claims. The study covers various activities, including dataset collection, dataset pre-processing, architecture selection, setup of settings, model training (fine-tuning), model testing, and implementation. The study includes a comprehensive analysis of different models, with a special focus on multilingual models where the same model is capable of processing social media posts in both English and in low-resource languages such as Arabic, Bulgarian, Dutch, Polish, Czech, Slovak. The results obtained from the study were validated against state-of-the-art models, and the comparison demonstrated the robustness of the proposed models. The novelty of this work lies in the development of multi-label multilingual classification models that can simultaneously detect harmful posts and posts that contain verifiable factual claims in an efficient way.

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