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International Conference On Big Data and Internet of Things

BDIoT 2021: Proceedings of the 5th International Conference on Big Data and Internet of Things pp 581–599Cite as

User-Enriched Embedding for Fake News Detection on Social Media

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 489))

Abstract

Recent political, pandemic, and social turmoil events have led to an increase in the popularity and spread of misinformation. As demonstrated by the widespread effects of the large onset of fake news, humans are inconsistent if not outright poor detectors of fake news. Thereby, many efforts are being made to automate the process of fake news detection. The most popular of these approaches include blacklisting sources and authors that are unreliable. While these tools are useful, in order to create a more complete end to end solution, we need to account for more difficult cases where reliable sources and authors release fake news. As such, the goal of this paper is to propose an approach for detecting the language and behavioral patterns that characterize fake and real news through the use of social network analysis and natural language processing techniques. We have built a model that catches many intuitive indications of real and fake news using users and submissions attributes, and thus laid the foundation for an approach that concatenates multiple embeddings for better fake news detection on social media.

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Author information

Authors and Affiliations

  1. ENSIAS, Mohammed V University in Rabat, Rabat, Morocco

    Oussama Hebroune & Lamia Benhiba

Authors
  1. Oussama Hebroune
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  2. Lamia Benhiba
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Corresponding author

Correspondence to Oussama Hebroune .

Editor information

Editors and Affiliations

  1. ENSIAS, Mohammed V University, Rabat, Morocco

    Mohamed Lazaar

  2. UNILEHAVRE, UNIROUEN, Normandie Université, Le Havre, France

    Claude Duvallet

  3. Vrije Universiteit Brussel, Brussels, Belgium

    Abdellah Touhafi

  4. ENSA, Abdelmalek Essaâdi University, Tetuan, Morocco

    Mohammed Al Achhab

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Hebroune, O., Benhiba, L. (2022). User-Enriched Embedding for Fake News Detection on Social Media. In: Lazaar, M., Duvallet, C., Touhafi, A., Al Achhab, M. (eds) Proceedings of the 5th International Conference on Big Data and Internet of Things. BDIoT 2021. Lecture Notes in Networks and Systems, vol 489. Springer, Cham. https://doi.org/10.1007/978-3-031-07969-6_44

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