Grace Guo, Lifu Deng, et al.
FAccT 2024
To defend against machine-generated fake news, an effective mechanism is urgently needed. We contribute a novel benchmark for fake news detection at the knowledge element level, as well as a solution for this task which incorporates cross-media consistency checking to detect the fine-grained knowledge elements making news articles misinformative. Due to training data scarcity, we also formulate a novel data synthesis method by manipulating knowledge elements within the knowledge graph to generate noisy training data with specific, hard to detect, known inconsistencies. Our detection approach outperforms the state-of-the-art (up to 16.8% accuracy gain), and more critically, yields fine-grained explanations.
Grace Guo, Lifu Deng, et al.
FAccT 2024
Taku Ito, Luca Cocchi, et al.
ICML 2025
Natalia Martinez Gil, Kanthi Sarpatwar, et al.
NeurIPS 2023
Sara Rosenthal, Pepa Atanasova, et al.
ACL-IJCNLP 2021