10 citations · 19 across the 12 of their papers we have counts for
12 papers
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…
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Hongzhan Lin, Ziyang Luo, Wei Gao +3
The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to…
GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse
Hongzhan Lin, Ziyang Luo, Bo Wang +2
The exponential growth of social media has profoundly transformed how information is created, disseminated, and absorbed, exceeding any precedent in the digital age. Regrettably, t…
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
Hongzhan Lin, Ziyang Luo, Jing Ma +1
The age of social media is rife with memes. Understanding and detecting harmful memes pose a significant challenge due to their implicit meaning that is not explicitly conveyed thr…
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
Ruichao Yang, Wei Gao, Jing Ma +2
In recent years, we witness the explosion of false and unconfirmed information (i.e., rumors) that went viral on social media and shocked the public. Rumors can trigger versatile,…
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
Hongzhan Lin, Jing Ma, Ruichao Yang +2
The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for mod…
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…
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
Hongzhan Lin, Jing Ma, Mingfei Cheng +3
Rumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection metho…