Presented at the ACL '22 Workshop Conference Proceedings

May 9, 2022·
Jason Lucas
Jason Lucas
· 1 min read
Image credit: Limeng Cui
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.
Location

The Convention Centre Dublin

8 Sentosa Gateway, Dublin, Europe

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Jason Lucas
Authors
Jason Lucas
Ph.D. Candidate in Informatics

I am a PhD candidate in Informatics in the College of IST at Penn State University, where I conduct research at the PIKE Research Lab under the guidance of Dr. Dongwon Lee. I specialize in AI/ML research focused on Information Integrity, Safe and Ethical AI, including combating harmful content across multiple languages and modalities. My research spans low-resource multilingual NLP, generative AI, and adversarial machine learning, with work extending across 79 languages. I have published 12 papers with 260+ citations in premier venues including ACL, EMNLP, IEEE, and NAACL.

My doctoral research focuses on bridging the digital language divide through transfer learning, classification (NLU), generation (NLG), adversarial attacks, and developing end-to-end AI pipelines using RAG and Agentic AI workflows for combating multilingual threats. Drawing from my Grenadian background and knowledge of local Creole languages, I bring a global perspective to AI challenges, working to democratize state-of-the-art AI capabilities for underserved linguistic communities worldwide. My mission is to develop robust multilingual multimodal systems and mitigate evolving security vulnerabilities while enhancing access to human language technology through cutting-edge solutions.

As an NSF LinDiv Fellow, I conduct transdisciplinary research advancing human-AI language interaction for social good. I actively mentor 5+ research interns and teach Applied Generative AI courses. Through industry experience at Lawrence Livermore National Lab, Interaction LLC, and Coalfire, I bridge academic research with practical applications in combating evolving security threats and enhancing global AI accessibility. I see multilingual advances and interdisciplinary collaboration as a competitive advantage, not a communication challenge. Beyond research, I stay active through dance, fitness, martial arts, and community service.