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2022 ACL ACL 2022

Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands

Abstract

AbstractThe COVID-19 pandemic has created threats to global health control. Misinformation circulated on social media and news outlets has undermined public trust towards Government and health agencies. This problem is further exacerbated in developing countries or low-resource regions, where the news is not equipped with abundant English fact-checking information. In this paper, we make the first attempt to detect COVID-19 misinformation (in English, Spanish, and Haitian French) populated in the Caribbean regions, using the fact-checked claims in the US (in English). We started by collecting a dataset of Caribbean real & fake claims. Then we trained several classification and language models on COVID-19 in the high-resource language regions and transferred the knowledge to the Caribbean claim dataset. The experimental results of this paper reveal the limitations of current fake claim detection in low-resource regions and encourage further research on multi-lingual detection.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Jason Lucas , Limeng Cui , Thai Le , Dongwon Lee

Topics

Artificial Intelligence > Learning Paradigms > Transfer Learning Machine Learning > Core Methods > Classification Natural Language Processing > Applications > Fact-Checking Machine Learning > Learning Paradigms > Transfer Learning Machine Learning > Learning Types > Multi-Lingual Learning

Keywords

domain adaptation multilingual nlp cross-lingual transfer low-resource language multilingual model misinformation detection
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