6 papers
Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns
Sishun Liu, Sajal Halder, Ke Deng +2
Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by hu…
Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processes
Sishun Liu, Ke Deng, Yongli Ren +2
Marked Temporal Point Process (MTPP) has been well studied to model the event distribution in marked event streams, which can be used to predict the mark and arrival time of the ne…
Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoning
Sishun Liu, Ke Deng, Xiuzhen Zhang +1
Neural network-based Marked Temporal Point Process (MTPP) models have been widely adopted to model event sequences in high-stakes applications, raising concerns about the trustwort…
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…
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,…
Explainable History Distillation by Marked Temporal Point Process
Sishun Liu, Ke Deng, Yan Wang +1
Explainability of machine learning models is mandatory when researchers introduce these commonly believed black boxes to real-world tasks, especially high-stakes ones. In this pape…