Supervised Learning for Fake News Detection

By Julio C. S. Reis, A. Correia, Fabricio Murai, Adriano Veloso, Fabrício Benevenuto, Erik Cambria. IEEE Intelligent Systems, 2019

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A large body of recent works has focused on understanding and detecting fake news stories that are disseminated on social media. To accomplish this goal, these works explore several types of features extracted from news stories, including source and posts from social media. In addition to exploring the main features proposed in the literature for fake news detection, we present a new set of features and measure the prediction performance of current approaches and features for automatic detection of fake news. Our results reveal interesting findings on the usefulness and importance of features for detecting false news. Finally, we discuss how fake news detection approaches can be used in the practice, highlighting challenges and opportunities.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/25586b8f46f773876e6e7f78d8dc70e48b8630a7