Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly. However, the usual supervised approach to debunking via misinformation classification requires human-annotated data and is not suited to the fast time-frame of newly emerging events such as the COVID-19 outbreak. In this paper, we postulate that misinformation itself has higher perplexity compared to truthful statements, and propose to leverage the perplexity to debunk false claims in an unsupervised manner. First, we extract reliable evidence from scientific and news sources according to sentence similarity to the claims. Second, we prime a language model with the extracted evidence and finally evaluate the correctness of given claims based on the perplexity scores at debunking time. We construct two new COVID-19-related test sets, one is scientific, and another is political in content, and empirically verify that our system performs favorably compared to existing systems. We are releasing these datasets publicly to encourage more research in debunking misinformation on COVID-19 and other topics.
VEX-Bench: Benchmarking Verification Complexity of LLM-Generated Misinformation
The University of Melbourne（墨尔本大学）；Deakin University（迪肯大学）；Singapore Management University（新加坡管理大学）；City University of Hong Kong（香港城市大学）；Queensland University of Technology（昆士兰理工大学）；Fudan University（复旦大学）
A New Dataset and Benchmark for Grounding Multimodal Misinformation
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（多媒体软件国家工程研究中心，计算机科学学院，武汉大学）；School of Computing, National University of Singapore（计算学院，新加坡国立大学）；School of Computer Science, Peking University（计算机科学学院，北京大学）；State Key Laboratory for Multimedia Information Processing, Peking University（多媒体信息处理国家重点实验室，北京大学）
CovidMis20: COVID-19 Misinformation Detection System on Twitter Tweets using Deep Learning Models
Saginaw Valley State University（萨吉诺谷州立大学）；The Hashemite University（哈希姆大学）；Western Michigan University（西密歇根大学）；University of Nebraska at Kearney（内布拉斯加大学卡尼尔分校）
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
American University（美利坚大学）；University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；University of Ottawa（渥太华大学）；University of North Carolina Wilmington（北卡罗来纳大学威尔明顿分校）
Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States
Indraprastha Institute of Information Technology, Delhi（因德拉普拉斯信息技术学院德里分校）；PES University, Bangalore（佩斯大学班加罗尔分校）；BITS Pilani, K.K. Birla Goa Campus（印度比尔拉科学技术学院果阿校区）
All That Glisters Is Not Gold: A Benchmark for Reference-Free Counterfactual Financial Misinformation Detection
University of Manchester（曼彻斯特大学）；Stevens Institute of Technology（史蒂文斯理工学院）；Columbia University（哥伦比亚大学）；The FinAI；Halmstad University（哈尔姆斯塔德大学）；New York University（纽约大学）；University of Florida（佛罗里达大学）；National Institute of Advanced Industrial Science and Technology（产业技术综合研究所）
