Gülselin GÜLER, Sedef GÜNDÜZ

46563

Deep Learning Based Fake News Detection on Social Media

Deep Learning Based Fake News Detection on Social Media

Social media platforms become indispensable channels to discover the latest news by the Internet users. Millions of news is broken first, spread faster, and reach larger communities on these platforms in a very short time compared to traditional media organs. However, in contrast to traditional media, social media platforms lack of security in terms of control mechanisms to verify the reliability and accuracy of the disseminated news. This brings the need for automatic fake news detection systems for these platforms to prevent or reduce spread of false information. In this paper, we study the problem of fake news detection on social media for two languages, both of them having distinct linguistic features: Turkish and English. In this regard, we create the first real-world public dataset of Turkish fake and real news tweets, named SOSYalan, to the best of our knowledge. For English language, we carry out experiments with two benchmark datasets, BuzzFeed and ISOT. We develop deep learning based fake news detection systems for both of Turkish and English languages based on convolutional neural networks (CNN), and recurrent neural networks-long short term memory (RNN-LSTM) approaches with Word2vec embedding model. We evaluate the developed systems in terms of accuracy, precision, recall, F1-score, true-negative rate, false-positive rate, and false-negative rate metrics. The results demonstrate that the developed systems for English language produce higher accuracy rates compared to the most of the existing state-of-the-art studies. Additionally, the results confirm the superiority of our systems developed for Turkish language in comparison to very few studies conducted in this area.
Keywords:

fake news detection, social media deep learning, information security,

PDF

___

  • [1] S. Vermeer, D. Trilling, S. Kruikemeier, and C. de Vreese, “Online news user journeys: the role of social media, news websites, and topics,” Digital Journalism, vol. 8, no. 9, pp. 1114–1141, 2020.
  • [2] P. Jiao, A. Veiga, and A. Walther, “Social media, news media and the stock market,” Journal of Economic Behavior & Organization, vol. 176, pp. 63–90, 2020.
  • [3] S. R. Sahoo and B. B. Gupta, “Multiple features based approach for automatic fake news detection on social networks using deep learning,” Applied Soft Computing, vol. 100, p. 106983, 2021.
  • [4] D. H. Solomon, R. Bucala, M. J. Kaplan, and P. A. Nigrovic, “The “infodemic” of covid-19,” Arthritis & Rheumatology, vol. 72, no. 11, pp. 1806–1808, 2020.
  • [5] N. Newman, R. Fletcher, A. Schulz, S. Andi, C. T. Robertson, and R. K. Nielsen, “Reuters institute digital news report 2021,” Reuters Institute for the study of Journalism, 2021.
  • [6] U. Merto˘glu and B. Genc¸, “Automated fake news detection in the age of digital libraries,” Information Technology and Libraries, vol. 39, no. 4, 2020.
  • [7] N. Deligiannis, T. Huu, D. M. Nguyen, and X. Luo, “Deep learning for geolocating social media users and detecting fake news,” in NATO Workshop, 2018.
  • [8] S. G. Taskin, E. U. Kucuksille, and K. Topal, “Detection of turkish fake news in twitter with machine learning algorithms,” Arabian Journal for Science and Engineering, vol. 47, no. 2, pp. 2359–2379, 2022.
  • [9] S. B. Parikh and P. K. Atrey, “Media-rich fake news detection: A survey,” in 2018 IEEE conference on multimedia information processing and retrieval (MIPR). IEEE, 2018, pp. 436–441.
  • [10] A. Gupta, R. Sukumaran, K. John, and S. Teki, “Hostility detection and covid-19 fake news detection in social media,” arXiv preprint arXiv:2101.05953, 2021.
  • [11] P. H. A. Faustini and T. F. Cov˜oes, “Fake news detection in multiple platforms and languages,” Expert Systems with Applications, vol. 158, p. 113503, 2020.
  • [12] I. Ahmad, M. Yousaf, S. Yousaf, and M. O. Ahmad, “Fake news detection using machine learning ensemble methods,” Complexity, vol. 2020, 2020.
  • [13] F. A. Ozbay and B. Alatas, “Fake news detection within online social media using supervised artificial intelligence algorithms,” Physica A: Statistical Mechanics and its Applications, vol. 540, p. 123174, 2020.
  • [14] K. Shu, D. Mahudeswaran, S. Wang, D. Lee, and H. Liu, “Fakenewsnet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media,” Big data, vol. 8, no. 3, pp. 171–188, 2020.
  • [15] E. Tacchini, G. Ballarin, M. L. Della Vedova, S. Moret, and L. De Alfaro, “Some like it hoax: Automated fake news detection in social networks,” arXiv preprint arXiv:1704.07506, 2017.
  • [16] R. K. Kaliyar, A. Goswami, and P. Narang, “Fakebert: Fake news detection in social media with a bert-based deep learning approach,” Multimedia tools and applications, vol. 80, no. 8, pp. 11 765–11 788, 2021.
  • [17] R. K. Kaliyar, A. Goswami, and . P. Narang, “Echofaked: improving fake news detection in social media with an efficient deep neural network,” Neural computing and applications, vol. 33, pp. 8597–8613, 2021.
  • [18] Y. Han, S. Karunasekera, and C. Leckie, “Graph neural networks with continual learning for fake news detection from social media,” arXiv preprint arXiv:2007.03316, 2020.
  • [19] E. Okoro, B. Abara, A. Umagba, A. Ajonye, and Z. Isa, “A hybrid approach to fake news detection on social media,” Nigerian Journal of Technology, vol. 37, no. 2, pp. 454–462, 2018.
  • [20] F. Monti, F. Frasca, D. Eynard, D. Mannion, and M. M. Bronstein, “Fake news detection on social media using geometric deep learning,” arXiv preprint arXiv:1902.06673, 2019.
  • [21] J. C. Reis, A. Correia, F. Murai, A. Veloso, and F. Benevenuto, “Supervised learning for fake news detection,” IEEE Intelligent Systems, vol. 34, no. 2, pp. 76–81, 2019.
  • [22] T. Jiang, J. P. Li, A. U. Haq, A. Saboor, and A. Ali, “A novel stacking approach for accurate detection of fake news,” IEEE Access, vol. 9, pp. 22 626–22 639, 2021.
  • [23] M. H. Goldani, R. Safabakhsh, and S. Momtazi, “Convolutional neural network with margin loss for fake news detection,” Information Processing & Management, vol. 58, no. 1, p. 102418, 2021.
  • [24] M. H. Goldani, S. Momtazi, and R. Safabakhsh, “Detecting fake news with capsule neural networks,” Applied Soft Computing, vol. 101, p. 106991, 2021.
  • [25] S. Hakak, M. Alazab, S. Khan, T. R. Gadekallu, P. K. R. Maddikunta, and W. Z. Khan, “An ensemble machine learning approach through effective feature extraction to classify fake news,” Future Generation Computer Systems, vol. 117, pp. 47– 58, 2021.
  • [26] D. K. Vishwakarma, D. Varshney, and A. Yadav, “Detection and veracity analysis of fake news via scrapping and authenticating the web search,” Cognitive Systems Research, vol. 58, pp. 217– 229, 2019.
  • [27] E. Bozkanat, “Detecting fake news on social media: The case of turkey,” in Analyzing Global Social Media Consumption. IGI Global, 2021, pp. 49–67.
  • [28] M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, “A stylometric inquiry into hyperpartisan and fake news,” arXiv preprint arXiv:1702.05638, 2017.
  • [29] H. Ahmed, I. Traore, and S. Saad, “Detecting opinion spams and fake news using text classification,” Security and Privacy, vol. 1, no. 1, p. e9, 2018.
  • [30] S. Gunduz, F. Demirhan, and S. Sagiroglu, “Investigating sentimental relation between social media presence and academic success of turkish universities,” in 2014 13th International Conference on Machine Learning and Applications. IEEE, 2014, pp. 574–579.
  • [31] 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, 2013.
  • [32] B. Jang, I. Kim, and J. W. Kim, “Word2vec convolutional neural networks for classification of news articles and tweets,” PloS one, vol. 14, no. 8, p. e0220976, 2019.
  • [33] A. Kutuzov, M. Fares, S. Oepen, and E. Velldal, “Word vectors, reuse, and replicability: Towards a community repository of large-text resources,” in Proceedings of the 58th Conference on Simulation and Modelling. Link¨oping University Electronic Press, 2017, pp. 271–276.
  • [34] M. Zulqarnain, R. Ghazali, Y. M. M. Hassim, and M. Rehan, “A comparative review on deep learning models for text classification,” Indones. J. Electr. Eng. Comput. Sci, vol. 19, no. 1, pp. 325–335, 2020.
  • [35] S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep learning–based text classification: a comprehensive review,” ACM Computing Surveys (CSUR), vol. 54, no. 3, pp. 1–40, 2021.
  • [36] V. Q. Nguyen, T. N. Anh, and H.-J. Yang, “Real-time event detection using recurrent neural network in social sensors,” International Journal of Distributed Sensor Networks, vol. 15, no. 6, p. 1550147719856492, 2019.
  • [37] A. Mariyam, S. A. H. Basha, and S. V. Raju, “A literature survey on recurrent attention learning for text classification,” in IOP Conference Series: Materials Science and Engineering, vol. 1042, no. 1. IOP Publishing, 2021, p. 012030.
  • [38] A. B. Yolcu and S. Demirci, “Deep learning based classification of turkish news posts in twitter,” in 2021 6th International Conference on Computer Science and Engineering (UBMK). IEEE, 2021, pp. 298–303.
  • [39] P. Bahad, P. Saxena, and R. Kamal, “Fake news detection using bi-directional lstm-recurrent neural network,” Procedia Computer Science, vol. 165, pp. 74–82, 2019.
International Journal of Information Security Science-Cover
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2012
  • Yayıncı: Şeref SAĞIROĞLU
Arşiv
Sayıdaki Diğer Makaleler

Deep Learning Based Fake News Detection on Social Media

Gülselin GÜLER, Sedef GÜNDÜZ