601 citations · 2k across the 51 of their papers we have counts for
52 papers
Can LLM-Generated Misinformation Be Detected?
Canyu Chen, Kai Shu
The advent of Large Language Models (LLMs) has made a transformative impact. However, the potential that LLMs such as ChatGPT can be exploited to generate misinformation has posed…
Emulating Reader Behaviors for Fake News Detection
Junwei Yin, Min Gao, Kai Shu +3
The wide dissemination of fake news has affected our lives in many aspects, making fake news detection important and attracting increasing attention. Existing approaches make subst…
MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection
Hao Liao, Jiaohao Peng, Zhanyi Huang +4
The ease of spreading false information online enables individuals with malicious intent to manipulate public opinion and destabilize social stability. Recently, fake news detectio…
Data Augmentation for Seizure Prediction with Generative Diffusion Model
Kai Shu, Le Wu, Yuchang Zhao +3
Data augmentation (DA) can significantly strengthen the electroencephalogram (EEG)-based seizure prediction methods. However, existing DA approaches are just the linear transformat…
Investigating Gender Euphoria and Dysphoria on TikTok: Characterization and Comparison
SJ Dillon, Yueqing Liang, H. Russell Bernard +1
With the emergence of short video-sharing platforms, engagement with social media sites devoted to opinion and knowledge dissemination has rapidly increased. Among these platforms,…
MetaGAD: Meta Representation Adaptation for Few-Shot Graph Anomaly Detection
Xiongxiao Xu, Kaize Ding, Canyu Chen +1
Graph anomaly detection has long been an important problem in various domains pertaining to information security such as financial fraud, social spam and network intrusion. The maj…
TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks
Baixiang Huang, Bryan Hooi, Kai Shu
Road safety is a major global public health concern. Effective traffic crash prediction can play a critical role in reducing road traffic accidents. However, Existing machine learn…
Attacking Fake News Detectors via Manipulating News Social Engagement
Haoran Wang, Yingtong Dou, Canyu Chen +3
Social media is one of the main sources for news consumption, especially among the younger generation. With the increasing popularity of news consumption on various social media pl…
Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks
Ujun Jeong, Kaize Ding, Lu Cheng +3
Nowadays, fake news easily propagates through online social networks and becomes a grand threat to individuals and society. Assessing the authenticity of news is challenging due to…
Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues
Canyu Chen, Haoran Wang, Matthew Shapiro +3
Social media has been one of the main information consumption sources for the public, allowing people to seek and spread information more quickly and easily. However, the rise of v…
When Fairness Meets Privacy: Fair Classification with Semi-Private Sensitive Attributes
Canyu Chen, Yueqing Liang, Xiongxiao Xu +5
Machine learning models have demonstrated promising performance in many areas. However, the concerns that they can be biased against specific demographic groups hinder their adopti…
Memory-Guided Multi-View Multi-Domain Fake News Detection
Yongchun Zhu, Qiang Sheng, Juan Cao +5
The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a single domain (e.g., poli…
Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo
Qiang Sheng, Juan Cao, H. Russell Bernard +3
False news that spreads on social media has proliferated over the past years and has led to multi-aspect threats in the real world. While there are studies of false news on specifi…
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs
Kay Liu, Yingtong Dou, Yue Zhao +12
Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years…
Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approach
Yueqing Liang, Canyu Chen, Tian Tian +1
Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discriminati…
PromptDA: Label-guided Data Augmentation for Prompt-based Few-shot Learners
Canyu Chen, Kai Shu
Recent advances in large pre-trained language models (PLMs) lead to impressive gains in natural language understanding (NLU) tasks with task-specific fine-tuning. However, directly…
PyGOD: A Python Library for Graph Outlier Detection
Kay Liu, Yingtong Dou, Xueying Ding +5
PyGOD is an open-source Python library for detecting outliers in graph data. As the first comprehensive library of its kind, PyGOD supports a wide array of leading graph-based meth…
Domain Adaptive Fake News Detection via Reinforcement Learning
Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu +2
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate…
"This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders
Xinyi Zhou, Kai Shu, Vir V. Phoha +2
Individuals can be misled by fake news and spread it unintentionally without knowing it is false. This phenomenon has been frequently observed but has not been investigated. Our ai…
A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues
A. R. Sana Ullah, Anupam Das, Anik Das +2
Misinformation during pandemic situations like COVID-19 is growing rapidly on social media and other platforms. This expeditious growth of misinformation creates adverse effects on…
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach
Zhao Wang, Kai Shu, Aron Culotta
Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In th…
WALNUT: A Benchmark on Semi-weakly Supervised Learning for Natural Language Understanding
Guoqing Zheng, Giannis Karamanolakis, Kai Shu +1
Building machine learning models for natural language understanding (NLU) tasks relies heavily on labeled data. Weak supervision has been proven valuable when large amount of label…
Labeled Data Generation with Inexact Supervision
Enyan Dai, Kai Shu, Yiwei Sun +1
The recent advanced deep learning techniques have shown the promising results in various domains such as computer vision and natural language processing. The success of deep neural…
User Preference-aware Fake News Detection
Yingtong Dou, Kai Shu, Congying Xia +2
Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing…