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

In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. Most approaches to misinformation detection are monolingual, focused on high-resource languages, i.e., a handful of world languages that have benefited from substantial research investment. This survey provides a comprehensive overview of the current research on misinformation detection in low-resource languages, both in monolingual and multilingual settings. We review existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, and real-world applications. We examine emerging approaches, such as language-generalizable models and multi-modal techniques, and emphasize the need for improved data collection practices, interdisciplinary collaboration, and stronger incentives for socially responsible AI research. Our findings underscore the importance of systems capable of addressing misinformation across diverse linguistic and cultural contexts.

@article{wang2025_2410.18390,
  title={ Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey },
  author={ Xinyu Wang and Wenbo Zhang and Sarah Rajtmajer },
  journal={arXiv preprint arXiv:2410.18390},
  year={ 2025 }
}