Fake News Detection Using Deep Learning

  • Received : 2018.06.22
  • Accepted : 2018.10.31
  • Published : 2019.10.31


With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.


Artificial Intelligence;Fake News Detection;Natural Language Processing


  1. Y. Yoon, T. Eom, J. Ahn, H. Lee, and J. Heo, "Survey of fake news detection technology," IITP Weekly Trend, vol. 1816, pp. 12-23, 2017.
  2. C. Silverman, "This analysis shows how viral fake election news stories outperformed real news On Facebook," 2016 [Online]. Available:
  3. The Trust Project, "News with integrity," [Online]. Available:
  4. S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, "Prominent features of rumor propagation in online social media," in Proceedings of 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, 2013, pp. 1103-1108.
  5. W. Largent, "Talos targets disinformation with fake news challenge victory," 2017 [Online]. Available:
  6. A. Hanselowski, "Team Athene on the fake news challenge," 2017 [Online]. Available:
  7. P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," 2016 [Online]. Available:
  8. Y. Kim, "Convolutional neural networks for sentence classification," 2014 [Online]. Available:
  9. T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word representations in vector space," 2013 [Online]. Available:
  10. W. J. Kim, D. H. Kim, and H. W. Jang, "Semantic extention search for documents using the Word2vec," The Journal of the Korea Contents Association, vol. 16, no. 10, pp. 687-692, 2016.
  11. T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, "Distributed representations of words and phrases and their compositionality," Advances in Neural Information Processing Systems, vol. 26, pp. 3111-3119, 2013.
  12. C. D. Santos, M. Tan, B. Xiang, and B. Zhou, "Attentive pooling networks," 2016 [Online]. Available:
  13. K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. S. Zemel, and Y. Bengio, "Show, attend and tell: neural image caption generation with visual attention," in Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 2015, pp. 2048-2057.
  14. D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," in Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, 2015.
  15. M. Maimaiti, A. Wumaier, K. Abiderexiti, and T. Yibulayin, "Bidirectional long short-term memory network with a conditional random field layer for Uyghur part-of-speech tagging," Information, vol. 8, no. 4, article no. 157, 2017.
  16. A. Graves, A. R. Mohamed, and G. Hinton, "Speech recognition with deep recurrent neural networks," in Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013, pp. 6645-6649.
  17. W. T. Yih, X. He, and C. Meek, "Semantic parsing for single-relation question answering," in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Baltimore, MD, 2014, pp. 643-648.
  18. K. A. Spackman, "Signal detection theory: Valuable tools for evaluating inductive learning," in Proceedings of the 6th International Workshop on Machine Learning, Ithaca, NY, 1989, pp. 160-163.
  19. P. M. Sosa, "Twitter sentiment analysis using combined LSTM-CNN models," 2018 [Online]. Available: