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FN2: Fake News DetectioN Based on Textual and Contextual Features

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Information and Communications Security (ICICS 2022)


Fake news is a serious concern that has received a lot of attention lately due to its harmful impact on society. In order to limit the spread of fake news, researchers have proposed automated ways to identify fake news articles using artificial intelligence and neural network models. However, existing methods do not achieve a high level of accuracy, which hinders their efficacy in real life. To this end, we introduce FN2 (Fake News detectioN): a novel neural-network based framework that combines both textual and contextual features of the news articles. Among the many unique features of FN2, it utilizes a set of explicit contextual features that are easy to collect and already available in the raw user metadata. To evaluate the accuracy of our classification model, we collected a real dataset from a fact-checking website, comprising over 16 thousand politics-related news articles. Our experimental results show that FN2 improves the accuracy by at least \(13\%\), compared to current state-of-the-art approaches. Moreover, it achieves better classification results than the existing models. Finally, preliminary results also show that FN2 provides a quite good generalization—outperforming competitors—also when applied to a qualitatively different data-set (entertainment news). The novelty of the approach, the staggering quantitative results, its versatility, as well as the discussed open research issues, have a high potential to open up novel research directions in the field.

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The authors would like to thank the reviewers that, with their comments, helped to improve the quality of the paper, and Dr. Chuan Yue for shepherding this contribution.

This work was partially supported by NPRP-S-11-0109-180242, from the QNRF-Qatar National Research Fund, a member of The Qatar Foundation. The information and views set out in this publication are those of the authors and do not necessarily reflect the official opinion of the QNRF.

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Correspondence to Mouna Rabhi .

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Rabhi, M., Bakiras, S., Di Pietro, R. (2022). FN2: Fake News DetectioN Based on Textual and Contextual Features. In: Alcaraz, C., Chen, L., Li, S., Samarati, P. (eds) Information and Communications Security. ICICS 2022. Lecture Notes in Computer Science, vol 13407. Springer, Cham.

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