Detecting Fake News on Social Media
In the past decade, social media is becoming increasingly popular for news consumption due to its easy access, fast dissemination and low cost. However, social media also enables the wide propagation of "fake news," i.e., news with intentionally false information. Fake news on social media can have significant negative societal effects. Therefore, fake news detection on social media has recently become an emerging research area that is attracting tremendous attention. This lecture, from a data mining perspective, introduces the basic concepts and characteristics of fake news across disciplines, reviews representative fake news detection methods in a principled way and illustrates challenging issues of fake news detection on social media. In particular, we discussed the value of news content and social context, and important extensions to handle early detection, weakly supervised detection and explainable detection. The concepts, algorithms and methods described in this lecture can help harness the power of social media to build effective and intelligent fake news detection systems. This book is an accessible introduction to the study of detecting fake news on social media. It is an essential read for students, researchers and practitioners to understand, manage and excel in this area.
Bios
Kai Shu is a doctoral student and research assistant at Data Mining and Machine Learning (DMML) Lab at ASU. His research interests include fake news detection, social computing, data mining and machine learning. He has published innovative works in highly ranked journals and interned at Yahoo! Research and Microsoft Research.
Huan Liu is a professor of computer science and engineering. His research interests are in data mining, machine learning, social computing, artificial intelligence and investigating interdisciplinary problems in data-intensive applications. He is the founding organizer of the International Conference Series on Social Computing, Behavioral-Cultural Modeling and Prediction.
