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

ABSTRACT

The COVID-19 pandemic has created severe threats to global health control. In particular, misinformation circulated on social media and news outlets has undermined public trust in government and health agencies. This problem is further exacerbated in developing countries or low-resource regions where the news may not be equipped with abundant English fact-checking information. This poses a question "are existing computational solutions toward misinformation also effective in low-resource regions?" In this paper, to answer this question, we make the first attempt to detect COVID 19 misinformation in English, Spanish, and Haitian French populated in the Caribbean region, using the fact-checked claims in US-English. We started by collecting a dataset of real & false claims in the Caribbean region. Then we trained several classification and language models on COVID-19 from high-resource language regions and transferred this knowledge to the Caribbean claim dataset. The experimental results show the limitations of current false claim detection in low-resource regions and encourage further research toward the detection of multi-lingual false claims in long tail.