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Fake news detection on social media using Adaptive Optimization based Deep Learning Approach

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Abstract

Various content-sharing platforms and social media are developed in recent times so that it is highly possible to spread fake news and misinformation. This kind of news may cause chaos and panic among people. The automatic and accurate detection of fake news is a complex task. To consider this issue, an automatic and effective fake news detection approach is implemented. The proposed methodology is named Adaptive Adam Adadelta Optimizer-based Deep Bi-directional Long Short Term Memory (Bi-LSTM), in which Adaptive Adam AO is the merging of Adadelta Optimizer (AO), Adaptive concept, and Adam Optimization. The Bidirectional Encoder Representations from Transformers (BERT) is used for the tokenization and then, feature mining is done. Also, the dimensionality of the features is reduced by Singular Value Decomposition (SVD) and Moving Principal Component Analysis (MPCA). From them, the top-N feature selection is performed using Joint Quantum Entropy. Finally, for fake news detection, Adaptive Adam AO trained Deep Bi-LSTM is used, which obtained maximum specificity, sensitivity, and accuracy of 0.928, 0.945, and 0.938.

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Data availability

The data that support the findings of this study are openly available in Fake News Net database at https://github.com/KaiDMML/FakeNewsNet/tree/master/dataset and Buzz Feed News database at https://github.com/BuzzFeedNews/2016-10-facebook-fact-check/blob/master/data/facebook-fact-check.csv

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Authors and Affiliations

  1. Department of Computer Science, Hindustan Institute of Technology and Science, Chennai, India

    Steni Mol T S

  2. Department of Computer Applications, Vellore Institute of Technology, Chennai, India

    P S Sreeja

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  1. Steni Mol T S
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  2. P S Sreeja
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T S, S.M., Sreeja, P.S. Fake news detection on social media using Adaptive Optimization based Deep Learning Approach. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19073-3

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