Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey

• URL: http://arxiv.org/abs/2410.18390v1
• Date: Thu, 24 Oct 2024 03:02:03 GMT
• Title: Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey
• Authors: Xinyu Wang, Wenbo Zhang, Sarah Rajtmajer,
• Abstract summary: This survey provides a comprehensive overview of the current research on low-resource language misinformation detection. We review the existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, real-world applications, and research efforts. Our findings underscore the need for robust, inclusive systems capable of addressing misinformation across diverse linguistic and cultural contexts.
• Score: 2.5459710368096586
• License:
• Abstract: In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. While significant advances have been made in misinformation detection, the focus remains largely on monolingual high-resource contexts, with low-resource languages often overlooked. This survey aims to bridge that gap by providing a comprehensive overview of the current research on low-resource language misinformation detection in both monolingual and multilingual settings. We review the existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, real-world applications, and research efforts. We also examine emerging approaches, such as language-agnostic models and multi-modal techniques, while emphasizing the need for improved data collection practices, interdisciplinary collaboration, and stronger incentives for socially responsible AI research. Our findings underscore the need for robust, inclusive systems capable of addressing misinformation across diverse linguistic and cultural contexts.

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