Unseen Fake News Detection Through Casual Debiasing
The widespread dissemination of fake news on social media poses significant risks, necessitating timely and accurate detection. However, existing methods struggle with unseen news due to their reliance on training data from past events and domains, leaving the challenge of detecting novel fake news largely unresolved.
Authors
- Jianzhong Qi (ORCID: https://orcid.org/0000-0001-6501-9050)
- Shuzhi Gong (ORCID: https://orcid.org/0009-0007-9289-9015)
- Richard Sinnott (ORCID: https://orcid.org/0000-0001-5998-222X)
- Cécile Paris (ORCID: https://orcid.org/0000-0003-3816-0176)
Institutions
- Commonwealth Scientific and Industrial Research Organisation (AU)
- The University of Melbourne (AU)
- Data61 (AU)
Publication Details
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1145/3701716.3715517
- Primary Topic
- Misinformation and Its Impacts
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Commonwealth Scientific and Industrial Research Organisation