@inproceedings{4d5cb57ee41b486eb14ddc2dab3a142d,
title = "Fake news detection: An interdisciplinary research",
abstract = "The explosive growth of fake news and its erosion to democracy, journalism and economy has increased the demand for fake news detection. To achieve efficient and explainable fake news detection, an interdisciplinary approach is required, relying on scientific contributions from various disciplines, e.g., social sciences, engineering, among others. Here, we illustrate how such multidisciplinary contributions can help detect fake news by improving feature engineering, or by providing well-justified machine learning models. We demonstrate how news content, news propagation patterns, and users' engagements with news can help detect fake news.",
keywords = "Disinformation, Fake news detection, Fake news research, False news, Misinformation",
author = "Xinyi Zhou and Reza Zafarani",
year = "2019",
month = may,
day = "13",
doi = "10.1145/3308560.3316476",
language = "English (US)",
series = "The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019",
publisher = "Association for Computing Machinery, Inc",
booktitle = "The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019",
note = "2019 World Wide Web Conference, WWW 2019 ; Conference date: 13-05-2019 Through 17-05-2019",
}