The explosive growth of fake news and its erosion to democracy, justice, and public trust increases the demand for research on fake news. The goal of this tutorial is to
(I) clearly introduce the concept and characteristics of fake news and how it can be formally differentiated from other similar concepts such as false/satire news, mis-/dis-information, among others, which helps deepen the understanding of fake news;
(II) provide a comprehensive review of fundamental theories across disciplines and illustrate how they can be used to conduct interdisciplinary fake news research, facilitating a concerted effort of experts in computer and information science, political science, journalism, social science, psychology and economics. Such concerted efforts can result in highly efficient and interpretable fake news detection;
(III) systematically present fake news detection strategies from four perspectives (i.e., knowledge, style, propagation, and credibility) and the ways that each perspective utilizes techniques developed in data/graph mining, machine learning, natural language processing, information retrieval; and
(IV) detail open issues within current fake news studies to reveal its great potential research opportunities, hoping to attract researchers within a broader area to work on fake news detection and further facilitate its development.
The tutorial aims to promote a fair, healthy and safe online information and news dissemination ecosystem, hoping to attract more researchers, engineers and students with various interests to fake news research. Few prerequisite are required for KDD participants to attend.