@inproceedings{lucas-etal-2022-detecting,
    title = "\textbf{Detecting False Claims in Low-Resource Regions: A Case Study of {C}aribbean Islands}",
    author = "\textbf{Lucas, Jason}  and
      Cui, Limeng  and
      Le, Thai  and
      Lee, Dongwon",
    editor = "Chakraborty, Tanmoy  and
      Akhtar, Md. Shad  and
      Shu, Kai  and
      Bernard, H. Russell  and
      Liakata, Maria  and
      Nakov, Preslav  and
      Srivastava, Aseem",
    booktitle = "Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "\textbf{Association for Computational Linguistics}",
    url = "https://aclanthology.org/2022.constraint-1.11",
    doi = "10.18653/v1/2022.constraint-1.11",
    pages = "95--102",
    abstract = "The 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.",
}