The explosive growth of fake news and its erosion of democracy, justice, and public trust have made fake news detection and intervention an urgent research priority. This site accompanies our survey, A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities, which reviews and evaluates detection methods from four complementary perspectives: the false knowledge a story carries, its writing style, its propagation patterns, and the credibility of its source. Beyond surveying methods, we identify fundamental theories drawn from the social sciences, psychology, economics, journalism, and computer and information sciences, with the aim of grounding fake news research in an interdisciplinary foundation and enabling detection that is not only efficient but, more importantly, explainable. We also outline open problems and research opportunities meant to guide future work. As the field continues to evolve, this repository serves as a living companion to the survey—offering summaries, timely updates, tutorials, datasets, and related resources for researchers, engineers, and students entering the field.
Citation
Xinyi Zhou and Reza Zafarani. 2020. A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities. ACM Computing Surveys 53, 5, Article 109 (September 2020), 40 pages. https://doi.org/10.1145/3395046
BibTeX
@article{zhou2020survey,
author = {Zhou, Xinyi and Zafarani, Reza},
title = {A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities},
journal = {ACM Computing Surveys},
volume = {53},
number = {5},
articleno = {109},
numpages = {40},
year = {2020},
month = sep,
issn = {0360-0300},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3395046},
doi = {10.1145/3395046},
keywords = {fake news, news verification, disinformation, misinformation, fact-checking, knowledge graph, deception detection, information credibility, social media}
}