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Fake Detect: A Deep Learning Ensemble Model for Fake News Detection

Author

Listed:
  • Nida Aslam
  • Irfan Ullah Khan
  • Farah Salem Alotaibi
  • Lama Abdulaziz Aldaej
  • Asma Khaled Aldubaikil
  • M. Irfan Uddin

Abstract

Pervasive usage and the development of social media networks have provided the platform for the fake news to spread fast among people. Fake news often misleads people and creates wrong society perceptions. The spread of low-quality news in social media has negatively affected individuals and society. In this study, we proposed an ensemble-based deep learning model to classify news as fake or real using LIAR dataset. Due to the nature of the dataset attributes, two deep learning models were used. For the textual attribute “statement,†Bi-LSTM-GRU-dense deep learning model was used, while for the remaining attributes, dense deep learning model was used. Experimental results showed that the proposed study achieved an accuracy of 0.898, recall of 0.916, precision of 0.913, and F-score of 0.914, respectively, using only statement attribute. Moreover, the outcome of the proposed models is remarkable when compared with that of the previous studies for fake news detection using LIAR dataset.

Suggested Citation

  • Nida Aslam & Irfan Ullah Khan & Farah Salem Alotaibi & Lama Abdulaziz Aldaej & Asma Khaled Aldubaikil & M. Irfan Uddin, 2021. "Fake Detect: A Deep Learning Ensemble Model for Fake News Detection," Complexity, Hindawi, vol. 2021, pages 1-8, April.
    • Handle: RePEc:hin:complx:5557784
    DOI: 10.1155/2021/5557784
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    Cited by:

    1. Raffaele D’Ambrosio & Giuseppe Giordano & Serena Mottola & Beatrice Paternoster, 2021. "Stiffness Analysis to Predict the Spread Out of Fake Information," Future Internet, MDPI, vol. 13(9), pages 1-10, August.
    2. Noha Alnazzawi & Najlaa Alsaedi & Fahad Alharbi & Najla Alaswad, 2022. "Using Social Media to Detect Fake News Information Related to Product Marketing: The FakeAds Corpus," Data, MDPI, vol. 7(4), pages 1-13, April.
    3. Ciprian-Octavian Truică & Elena-Simona Apostol, 2023. "It’s All in the Embedding! Fake News Detection Using Document Embeddings," Mathematics, MDPI, vol. 11(3), pages 1-29, January.
    4. Paul Meddeb & Stefan Ruseti & Mihai Dascalu & Simina-Maria Terian & Sebastien Travadel, 2022. "Counteracting French Fake News on Climate Change Using Language Models," Sustainability, MDPI, vol. 14(18), pages 1-14, September.

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