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Fake news detection system based on modified bi-directional long short term memory

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Abstract

The use of social media has increased tremendously during the past few decades and is considered one of the major sources of news. Social media don’t have the authority to verify the authenticity of the news and because of this, it is considered as a significant reason behind the spread of fake news. Many existing methods have been applied to detect fake news in social media and all those have the drawbacks of lower efficiency and overfitting problem. In this research, structural features with the Modified Bi-directional Long Short Term Memory (MBi-LSTM) method are proposed to improve the efficiency of Fake news detection. The attention layer is introduced in the Bi-LSTM to update the weight value of the input features and Term Frequency – Inverse Document Frequency (TF-IDF), based on the scalar factor. This weight value is updated in the input gate weight value of the Bi-LSTM that helps to find the relevant feature to store in cell. The proper weight in the Bi-LSTM model stores the features related to reliable information in long-term that helps to improves the classification performance. The structural, user, content, and temporal features were extracted from the Twitter data and applied to the MBi-LSTM method. 33 features were extracted for structural, user, content, and temporal features for the classification. The PolitiFact dataset is collected and used for testing the efficiency of the proposed MBi-LSTM method. Additionally, the CREDBANK dataset is also applied to test the effectiveness of the proposed MBi-LSTM method in the case of a large dataset. The experimental result shows that the proposed MBi-LSTM method has an accuracy of 91% and the Bi-LSTM method has an accuracy of 86.69% in PolitiFact’s dataset.

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This study was not funded by any organization.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalay (UIT - RGPV), Bhopal, India

    Chetan Agrawal

  2. Department of Information Technology, University Institute of Technology Rajiv Gandhi Proudyogiki Vishwavidyalay (UIT - RGPV), Bhopal, India

    Anjana Pandey & Sachin Goyal

Authors
  1. Chetan Agrawal
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  2. Anjana Pandey
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  3. Sachin Goyal
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Correspondence to Chetan Agrawal.

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Buzzfeed

Shu, K., Sliva, A., Wang, S., Tang, J. and Liu, H., 2017. Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), pp.22-36.

CREDBANK

Mitra, T. and Gilbert, E., 2015, April. Credbank: A large-scale social media corpus with associated credibility annotations. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 9, No. 1).

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Agrawal, C., Pandey, A. & Goyal, S. Fake news detection system based on modified bi-directional long short term memory. Multimed Tools Appl 81, 24199–24223 (2022). https://doi.org/10.1007/s11042-022-12772-9

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