The Battlefront of Combating Misinformation and Coping with Media Bias
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the problem, there remain many ongoing challenges yet to be addressed, as can be witnessed from the plethora of untrue and harmful content present during the COVID-19 pandemic, which gave rise to the first social-media infodemic, and the international crises of late. In this tutorial, we provide researchers and practitioners with a systematic overview of the frontier in fighting misinformation. Specifically, we dive into the important research questions of how to (i) develop a robust fake news detection system that not only fact-checks information pieces provable by background knowledge, but also reason about the consistency and the reliability of subtle details about emerging events; (ii) uncover the bias and the agenda of news sources to better characterize misinformation; as well as (iii) correct false information and mitigate news biases, while allowing diverse opinions to be expressed. Participants will learn about recent trends, representative deep neural network language and multimedia models, ready-to-use resources, remaining challenges, future research directions, and exciting opportunities to help make the world a better place, with safer and more harmonic information sharing. © 2022 Owner/Author.
computation for the social good, correcting bias and misinformation, fake news detection, misinformation characterization, Deep neural networks, Fake detection, Multimedia systems, Public policy
Y.R. Fung, K.H. Huang, P. Nakov, and H. Ji, "The Battlefront of Combating Misinformation and Coping with Media Bias", in Proc. of the ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining (KDD 2022), Washington, Aug 2022, pp. 4790-4791, doi: 10.1145/3534678.3542615