The explosive growth of fake news and its erosion of democracy, justice, and public trust have increased the demand for research on fake news. The goal of this tutorial is to:
- 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 and mis-/dis-information, which helps deepen the understanding of fake news;
- 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;
- systematically present fake news detection strategies from four perspectives (i.e., knowledge, style, propagation, and credibility) and the ways each perspective utilizes techniques developed in data/graph mining, machine learning, natural language processing, and information retrieval; and
- detail open issues within current fake news studies to reveal 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 prerequisites are required to attend.
Citation
Reza Zafarani, Xinyi Zhou, Kai Shu, and Huan Liu. 2019. Fake News Research: Theories, Detection Strategies, and Open Problems. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’19). Association for Computing Machinery, New York, NY, USA, 3207–3208. https://doi.org/10.1145/3292500.3332287
BibTeX
@inproceedings{10.1145/3292500.3332287,
author = {Zafarani, Reza and Zhou, Xinyi and Shu, Kai and Liu, Huan},
title = {Fake News Research: Theories, Detection Strategies, and Open Problems},
year = {2019},
isbn = {9781450362016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3292500.3332287},
doi = {10.1145/3292500.3332287},
booktitle = {Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages = {3207--3208},
numpages = {2},
keywords = {disinformation, fake news, fake news detection, false news, misinformation, news verification, social media},
location = {Anchorage, AK, USA},
series = {KDD '19}
}