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

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Distributed Computing and Internet Technology (ICDCIT 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11969))

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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Notes

  1. 1.

    https://snap.stanford.edu/data/ego-Twitter.html.

  2. 2.

    snap-stanford/snap: Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.

  3. 3.

    http://compsocial.github.io/CREDBANK-data/.

  4. 4.

    Tweepy is open-sourced, hosted on GitHub and enables Python to communicate with Twitter platform and use its API.

  5. 5.

    https://networkx.github.io/.

  6. 6.

    https://gephi.org/.

  7. 7.

    https://www.mturk.com/.

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Author information

Authors and Affiliations

  1. National School for Computer Science, Manouba, Tunisia

    Tarek Hamdi & Hamda Slimi

  2. INSAT, LISI Research Laboratory, University of Carthage, Tunis, Tunisia

    Hamda Slimi, Ibrahim Bounhas & Yahya Slimani

Authors
  1. Tarek Hamdi
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  2. Hamda Slimi
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  3. Ibrahim Bounhas
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  4. Yahya Slimani
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Corresponding authors

Correspondence to Tarek Hamdi , Hamda Slimi or Ibrahim Bounhas .

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Editors and Affiliations

  1. Vietnam National University, Hanoi, Vietnam

    Dang Van Hung

  2. International Institute of Information Technology, Bangalore, India

    Meenakshi D´Souza

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Hamdi, T., Slimi, H., Bounhas, I., Slimani, Y. (2020). A Hybrid Approach for Fake News Detection in Twitter Based on User Features and Graph Embedding. In: Hung, D., D´Souza, M. (eds) Distributed Computing and Internet Technology. ICDCIT 2020. Lecture Notes in Computer Science(), vol 11969. Springer, Cham. https://doi.org/10.1007/978-3-030-36987-3_17

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  • DOI: https://doi.org/10.1007/978-3-030-36987-3_17

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