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2020 | OriginalPaper | Chapter

A Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embedding

Authors : Tarek Hamdi, Hamda Slimi, Ibrahim Bounhas, Yahya Slimani

Published in: Distributed Computing and Internet Technology

Publisher: Springer International Publishing

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Abstract

The quest for trustworthy, reliable and efficient sources of information has been a struggle long before the era of internet. However, social media unleashed an abundance of information and neglected the establishment of competent gatekeepers that would ensure information credibility. That’s why, great research efforts sought to remedy this shortcoming and propose approaches that would enable the detection of non-credible information as well as the identification of sources of fake news. In this paper, we propose an approach which permits to evaluate information sources in term of credibility in Twitter. Our approach relies on node2vec to extract features from twitter followers/followees graph. We also incorporate user features provided by Twitter. This hybrid approach considers both the characteristics of the user and his social graph. The results show that our approach consistently and significantly outperforms existent approaches limited to user features.

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Title: A Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embedding
Authors: Tarek Hamdi
Hamda Slimi
Ibrahim Bounhas
Yahya Slimani
Publisher: Springer International Publishing
Book: Distributed Computing and Internet Technology
Print ISBN: 978-3-030-36986-6

Electronic ISBN: 978-3-030-36987-3

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-36987-3_17

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