Work

Unseen Fake News Detection Through Casual Debiasing

Year: 2025

Type: conference-paper

Abstract: 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.

DOI: 10.1145/3701716.3715517

Authors: Shuzhi Gong, Richard Sinnott, Jianzhong Qi, Cecile Paris

Institutions: The University of Melbourne, Data61

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Funder: Commonwealth Scientific and Industrial Research Organisation

OpenAlex ID: W4410636971

FWCI: 13.9

Cites: 8

Cited by: 4

Related to: 10