Leveraging artificial intelligence to enhance the speed and scope of fact-checking
Project Description
The project, Leveraging Artificial Intelligence to Enhance the Speed and Scope of Fact-Checking, aims to transform the fight against misinformation with a focus on Chinese-language content, especially in Hong Kong. Addressing misinformation, rumours, and conspiracy theories — issues that have gained rapid traction across social media and digital platforms — this initiative recognises that traditional, manual fact-checking often struggles to keep pace. By integrating artificial intelligence, the project seeks to enhance the speed, efficiency, and reach of fact-checking, supporting both professional fact-checkers and empowering everyday users to critically assess the information they encounter.
This project stands out due to its multi-layered, interdisciplinary approach, bridging artificial intelligence, media studies, psychology, and communication research. Organised in four stages, the project covers identifying misinformation types, understanding how people process various information and fact-checking content, studying responses to AI-generated materials, and ultimately creating AI tools to assist fact-checkers and the public. Early successes include a typology framework of misinformation (Tsang, forthcoming) and published research on how people process misinformation (Tsang, 2021, 2022; Zhao & Tsang, 2024). Currently, the focus is on understanding audience perceptions of AI-generated misinformation and developing effective AI fact-checking tools for both professional and public use.
The project’s impact is already evident in Hong Kong through the HKBU Fact Check initiative, a recognised IFCN-certified fact-checking signatory. As one of the few such organisations in Hong Kong, it engages the public and provides a valuable resource for combating misinformation. The project’s findings have relevance beyond Hong Kong, addressing global challenges posed by misinformation and conspiracy theories. Its interdisciplinary framework not only advances academic understanding but also develops adaptable tools for international use. Additionally, this project addresses the evolving landscape of digital information, including AI-generated content. By studying how people process misinformation and AI-generated fact-checking, the project aims to equip individuals with critical media literacy skills. This initiative shows how AI can responsibly address urgent societal challenges, bridging technology and public good.
Project Investigator
Dr Stephanie Jean TSANG (Department of Communication Studies)
Funding/Award
- International Fact-Checking Network - ENGAGE 2023 Grant Award
- The Hong Kong Federation of Youth Groups - Media and Artificial Intelligence Literacy Research for Teachers and Students
Publications
- Tsang, S. (forthcoming). Misinformation, Disinformation, and Fake News? Proposing a New Typology Framework of False Information. Journalism. [Q1 in SSCI - Communication, 2023 JCR IF = 2.7]
- Zhao, X., & Tsang, S. (2024). How People Process Different Types of Misinformation on Social Media: A Taxonomy Based on Falsity Level and Evidence Type. Health Communication, 39(4), 741-753. https://doi.org/10.1080/10410236.2023.2184452 [Q1 in SSCI - Communication, 2023 JCR IF = 3.0]
- Tsang, S., Zheng, J., Li, W., & Salaudeen, M. (2023). An Experimental Study of the Effectiveness of Fact Checks: Interplay of Evidence Type, Veracity, and News Agreement. Online Information Review, 47(4), 1415-1429. https://doi.org/10.1108/OIR-09-2022-0492 [Q1 in SSCI - Information Science & Library Science, 2023 JCR IF = 3.1]
- Tsang, S. (2022). Issue Stance and Perceived Journalistic Motives Explain Divergent Audience Perceptions of Fake News. Journalism, 23(4), 823-840. https://doi.org/10.1177/1464884920926002 [Q1 in SSCI - Communication, 2023 JCR IF = 2.7]
- Tsang, S. (2021). Motivated Fake News Perception: The Impact of News Sources and Policy Support on Audiences’ Assessment of News Fakeness. Journalism & Mass Communication Quarterly, 98(4),1059-1077. https://doi.org/10.1177/1077699020952129 [Q1 in SSCI - Communication, 2023 JCR IF = 4.3]
