240 citations · 626 across the 19 of their papers we have counts for
10 papers · 1 filter
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection
Beizhe Hu, Qiang Sheng, Juan Cao +4
Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by t…
Improving Fake News Detection of Influential Domain via Domain- and Instance-Level Transfer
Qiong Nan, Danding Wang, Yongchun Zhu +4
Both real and fake news in various domains, such as politics, health, and entertainment are spread via online social media every day, necessitating fake news detection for multiple…
Generalizing to the Future: Mitigating Entity Bias in Fake News Detection
Yongchun Zhu, Qiang Sheng, Juan Cao +3
The wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake news of the…
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Yuting Yang, Pei Huang, Juan Cao +5
Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerab…
Quantifying Robustness to Adversarial Word Substitutions
Yuting Yang, Pei Huang, FeiFei Ma +4
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addres…
MDFEND: Multi-domain Fake News Detection
Qiong Nan, Juan Cao, Yongchun Zhu +2
Fake news spread widely on social media in various domains, which lead to real-world threats in many aspects like politics, disasters, and finance. Most existing approaches focus o…