10 citations · 11 across the 4 of their papers we have counts for
4 papers
Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques
Xiaofei Xu, Xiuzhen Zhang, Ke Deng
Fake news and misinformation poses a significant threat to society, making efficient mitigation essential. However, manual fact-checking is costly and lacks scalability. Large Lang…
Teaching Large Language Models Number-Focused Headline Generation With Key Element Rationales
Zhen Qian, Xiuzhen Zhang, Xiaofei Xu +1
Number-focused headline generation is a summarization task requiring both high textual quality and precise numerical accuracy, which poses a unique challenge for Large Language Mod…
Harnessing Network Effect for Fake News Mitigation: Selecting Debunkers via Self-Imitation Learning
Xiaofei Xu, Ke Deng, Michael Dann +1
This study aims to minimize the influence of fake news on social networks by deploying debunkers to propagate true news. This is framed as a reinforcement learning problem, where,…
Identifying Cost-effective Debunkers for Multi-stage Fake News Mitigation Campaigns
Xiaofei Xu, Ke Deng, Xiuzhen Zhang
Online social networks have become a fertile ground for spreading fake news. Methods to automatically mitigate fake news propagation have been proposed. Some studies focus on selec…