10 citations · 10 across the 4 of their papers we have counts for
6 papers · 1 filter
A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
Bo Wang, Jing Ma, Hongzhan Lin +4
Explainable fake news detection aims to assess the veracity of news claims while providing human-friendly explanations. Existing methods incorporating investigative journalism are…
Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models
Ruichao Yang, Wei Gao, Jing Ma +2
Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim…
Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom
Bo Wang, Jing Ma, Hongzhan Lin +4
Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justific…
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection
Zhiwei Yang, Jing Ma, Hechang Chen +3
Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances. However, they often tail…
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
Ziyang Luo, Yadong Xi, Jing Ma +4
Since 2017, the Transformer-based models play critical roles in various downstream Natural Language Processing tasks. However, a common limitation of the attention mechanism utiliz…
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning
Hongzhan Lin, Jing Ma, Liangliang Chen +3
Massive false rumors emerging along with breaking news or trending topics severely hinder the truth. Existing rumor detection approaches achieve promising performance on the yester…