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Abstract

The proliferation of fake news poses a significant threat to society, undermining trust in information and jeopardizing democratic processes. While the convolutional neural networks (CNNs) have shown promise in detecting fake news by analysing textual features, this study proposes a novel approach that integrates the CNNs with social learning theory. The authors argue that incorporating social signals, such as user credibility, social media engagement and network propagation patterns, can enhance the accuracy of fake news detection. The proposed hybrid model leverages the CNNs to extract textual features and combines them with social signals to capture the influence of social dynamics on the spread and perception of misinformation. We evaluate our model on a real-world dataset of news articles and social media interactions, demonstrating its effectiveness in improving fake news detection accuracy compared to traditional CNN-based approaches. The findings highlight the importance of considering social context in automated fake news detection and offer a promising direction for future researches.

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2026-02-04
2026-04-14

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References

  1. Aïmeur, E., Amri, S. and Brassard, G. (2023), ‘Fake news, disinformation and misinformation in social media: A review’, Social Network Analysis and Mining, 13: p. 30, https://doi.org/10.1007/s13278-023-01028-5.
     [Google Scholar]
  2. Akhtar, P., Ghouri, A. M., Khan, H. U. R., Amin ul Haq, M., Awan, U., Zahoor, N., Khan, Z. and Ashraf, A. (2023), ‘Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions’, Annals of Operations Research, 327, pp. 633–57, https://doi.org/10.1007/s10479-022-05015-5.
     [Google Scholar]
  3. Alghamdi, J., Luo, S. and Lin, Y. (2024), ‘A comprehensive survey on machine learning approaches for fake news detection’, Multimedia Tools and Applications, 83, pp. 51009–67, https://doi.org/10.1007/s11042-023-17470-8.
     [Google Scholar]
  4. Alharthi, W. and Alzahrani, M. E. (2024), ‘Toward an efficient hybrid clustering and classification approach for fake news detection in social network’, Indian Journal of Science and Technology, 17:26, pp. 2754–62, https://doi.org/10.17485/ijst/v17i26.579.
     [Google Scholar]
  5. Almarashy, A. H. J., Feizi-Derakhshi, M.-R. and Salehpour, P. (2023), ‘Enhancing fake news detection by multi-feature classification’, IEEE Access, 11, pp. 139601–613, https://doi.org/10.1109/access.2023.3339621.
     [Google Scholar]
  6. Al-Tarawneh, M. A. B., Al-irr, O., Al-Maaitah, K. S., Kanj, H. and Aly, W. H. F. (2024), ‘Enhancing fake news detection with word embedding: A machine learning and deep learning approach’, Computers, 13:9, https://doi.org/10.3390/computers13090239.
     [Google Scholar]
  7. Babatope, A. B. and Anil, V. K. S. (2024), ‘Cybersecurity on social media platforms: The use of deep learning methodologies for detecting anomalous user behavior’, GSC Advanced Research and Reviews, 21:02, pp. 38–47, https://doi.org/10.30574/gscarr.2024.21.2.0409.
     [Google Scholar]
  8. Barocas, S. and Selbst, A. D. (2016), ‘Big data’s disparate impact’, California Law Review, 104, pp. 671–732, http://dx.doi.org/10.2139/ssrn.2477899.
     [Google Scholar]
  9. Boniface, E. C., Agbo, J. C. and Udanor, C. N. (2024), ‘Optimizing fake news detection: An in-depth analysis of deep learning algorithm efficiency’, JNACS: Journal of Networking and Communication Systems, 7:3, pp. 9–19, https://doi.org/10.46253/jnacs.v7i3.a2.
     [Google Scholar]
  10. Boudana, S. and Segev, E. (2024), ‘Fake news makes the news: Definitions and framing of fake news in mainstream media’, Journalism Practice, pp. 1–20, https://doi.org/10.1080/17512786.2024.2379898.
     [Google Scholar]
  11. Canpolat, C. S. (2024), ‘Using artificial intelligence to detect violations and disinformation on social media networks, including intellectual property rights infringements’, Next Frontier For Life Sciences and AI, 8:1, pp. 73–75, https://doi.org/10.62802/q3zfyr63.
     [Google Scholar]
  12. Castillo, C., Mendoza, M. and Poblete, B. (2011), ‘Information credibility on Twitter’, in Proceedings of the 20th International Conference on World Wide Web (WWW ‘11), 28 March, New York: Association for Computing Machinery, pp. 675–84.
     [Google Scholar]
  13. Conneau, A., Kiela, D., Schwenk, H., Barrault, L. and Bordes, A. (2017), ‘Supervised learning of universal sentence representations from natural language inference data’, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, September, pp. 670–80.
     [Google Scholar]
  14. Della Vedova, M. L., Tacchini, E., Moret, S., Ballarin, G., DiPierro, M. and de Alfaro, L. (2018), ‘Automatic online fake news detection combining content and social signals’, 22nd Conference of Open Innovations Association (FRUCT), Jyväskylä, Finland, 15–18 May, IEEE, pp. 272–79.
     [Google Scholar]
  15. Dumitrache, A.-C. (2019), ‘Shades of fake news: Manifestation, effects and ways to combat false information’, Romanian Journal of Communication and Public Relations, 21:2, pp. 41–57, https://doi.org/10.21018/rjcpr.2019.2.276.
     [Google Scholar]
  16. Elmasry, A. and Talal, N. (2024), ‘Differentiating authentic news from fabrications using deep learning: A new approach’, Systems Assessment and Engineering Management, 1, pp. 45–53, https://doi.org/10.61356/j.saem.2024.1272.
     [Google Scholar]
  17. Fârte, G.-I. and Obadã, D.-R. (2021), ‘The effects of fake news on consumers’ brand trust: An exploratory study in the food security context’, Romanian Journal of Communication and Public Relations, 23:3, pp. 47–61, https://doi.org/10.21018/rjcpr.2021.3.333.
     [Google Scholar]
  18. Fionda, V. (2025), ‘Logic-based analysis of fake news diffusion on social media’, Social Network Analysis and Mining, 15, p. 59, https://doi.org/10.1007/s13278-025-01472-5.
     [Google Scholar]
  19. Ghosh, A., Sufian, A., Sultana, F., Chakrabarti, A. and De, D. (2020), ‘Fundamental concepts of convolutional neural network’, in V. E. Balas, R. Kumar and R. Srivastava (eds), Recent Trends and Advances in Artificial Intelligence and Internet of Things. Intelligent Systems Reference Library, vol. 172, Cham: Springer, pp. 519–67.
     [Google Scholar]
  20. Guess, A. M., Lockett, D., Lyons, B., Montgomery, J. M., Nyhan, B. and Reifler, J. (2020), ‘Fake news may have limited effects beyond increasing beliefs in false claims’, Harvard Kennedy School Misinformation Review, 1:1, https://doi.org/10.37016/mr-2020-004.
     [Google Scholar]
  21. Gupta, J., Mangal, D., Bansal, H. and Singhal, V. (2024), ‘Fake social media post detection by using deep learning algorithm’, International Research Journal of Computer Science, 11:05, pp. 483–89, https://doi.org/10.26562/irjcs.2024.v1105.09.
     [Google Scholar]
  22. Indolia, S., Goswamib, A. K., Mishrab, S. P. and Asopa, P. (2018), ‘Conceptual understanding of convolutional neural network: A deep learning approach’, Procedia Computer Science, 132, pp. 679–88, https://doi.org/10.1016/j.procs.2018.05.069.
     [Google Scholar]
  23. Kim, D.-H. and Lee, H.-Y. (2017), ‘Image manipulation detection using convolutional neural networks’, International Journal of Applied Engineering Research, 12:21, pp. 11640–46, https://www.researchgate.net/publication/322558428_Image_manipulation_detection_using_convolutional_neural_network. Accessed 19 January 2025.
     [Google Scholar]
  24. Mahmud, T., Hasan, I. T., Aziz, M., Rahman, T., Hossain, M. S. and Andersson, K. (2024), ‘Enhanced fake news detection through the fusion of deep learning and repeat vector representations’, Second International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India, 4–6 January, IEEE, pp. 654–60.
     [Google Scholar]
  25. Martin, A., Hinkelmann, K., Gerber, A., Lenat, D., van Harmelen, F. and Clark, P. (2019), ‘Combining machine learning with knowledge engineering to detect fake news in social networks: A survey’, in Proceedings of the AAAI 2019 Spring Symposium on Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019), Stanford University, Palo Alto, California, 25–27 March.
     [Google Scholar]
  26. Mintoo, A. A. (2024), ‘Detecting fake news using data analytics: A systematic literature review and machine learning approach’, Academic Journal on Innovation, Engineering and Emerging Technology, 1:1, pp. 108–30, https://doi.org/10.69593/ajieet.v1i01.143.
     [Google Scholar]
  27. Mol, T. S. S. and Gokila, S. (2024), ‘Optimization strategies in deep learning method ado for fake news detection on social media’, Eighth International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Türkiye, 21–22 September, IEEE, pp. 1–6.
     [Google Scholar]
  28. Monti, F., Frasca, F., Eynard, D., Mannion, D. and Bronstein, M. M. (2019), ‘Fake news detection on social media using geometric deep learning’, ArXiv, https://www.semanticscholar.org/paper/318d7da35307221267b6ce6ead995cc812245abb. Accessed 25 July 2025.
     [Google Scholar]
  29. Nagalakshmi, E. V., Vineeth, E. S., Goutham, Y. and Krishna, T. V. (2023), ‘Fake news detection using machine learning: A working model of fake news detection’, International Journal for Research in Applied Science and Engineering Technology, 11:5, pp. 1540–48, https://doi.org/10.22214/ijraset.2023.51637.
     [Google Scholar]
  30. Narayanan, M. B., Ramesh, A. K., Gayathri, K. S. and Shahina, A. (2023), ‘Fake news detection using a deep learning transformer-based encoder-decoder architecture’, Journal of Intelligent & Fuzzy Systems, 45:5, pp. 8001–13, https://doi.org/10.3233/jifs-223980.
     [Google Scholar]
  31. Neha, N. and Lepakshi, V. A. (2024), ‘Fake news detection’, IJRD: EPRA International Journal of Research & Development, 9:9, pp. 31–36, https://doi.org/10.36713/epra18130.
     [Google Scholar]
  32. Nguyen, V., Sugiyama, K., Nakov, P. and Kan, M.-Y. (2020), ‘FANG: Leveraging social context for fake news detection using graph representation’, in Proceedings of the 29th ACM International Conference on Information & Knowledge Management, Ireland, 19–23 October, New York: Association for Computing Machinery, pp. 1–10.
     [Google Scholar]
  33. Olagunju, A. A. and Awoyelu, I. O. (2024), ‘Performance evaluation of fake news detection models’, International Journal of Information Technology and Computer Science, 16:6, pp. 89–100, https://doi.org/10.5815/ijitcs.2024.06.07.
     [Google Scholar]
  34. Rajalakshmi, B. and Sebastian, N. (2024), ‘Fake news detection’, Indian Journal of Data Mining, 4:1, pp. 13–16, https://doi.org/10.54105/ijdm.a1638.04010524.
     [Google Scholar]
  35. Ren, Y., Wang, B., Zhang, J. and Chang, Y. (2020), ‘Adversarial active learning based heterogeneous graph neural network for fake news detection’, IEEE International Conference on Data Mining (ICDM), 1 November, Sorrento, Italy, pp. 452–61.
     [Google Scholar]
  36. Sanida, M. V., Sanida, T., Sideris, A., Dossis, M. and Dasygenis, M. (2024), ‘Fake news detection approach using hybrid deep learning framework’, Ninth South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Athens, Greece, 20–22 September, IEEE, pp. 81–84.
     [Google Scholar]
  37. Shaker, K. and Alqudsi, A. (2024), ‘Approach for detecting Arabic fake news using deep learning’, Iraqi Journal For Computer Science and Mathematics, 5:3, pp. 779–89, https://doi.org/10.52866/ijcsm.2024.05.03.049.
     [Google Scholar]
  38. Sukhramani, K., Kumre, H., Rasool, A. and Jadav, A. (2024), ‘Binary classification of news articles using deep learning’, IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India, 24–25 February, IEEE, pp. 1–9.
     [Google Scholar]
  39. Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S. and On, B.-W. (2020), ‘Fake news stance detection using deep learning architecture (CNN-LSTM)’, IEEE, 8, pp. 156695–706, https://doi.org/10.1109/ACCESS.2020.3019735.
     [Google Scholar]
  40. Vosoughi, S., Roy, D. and Aral, S. (2018), ‘The spread of true and false news online’, Science, 359:6380, pp. 1146–51, https://doi.org/10.1126/science.aap9559.
     [Google Scholar]
  41. Wardle, C. and Derakhshan, H. (2017), Information disorder: Toward an interdisciplinary framework for research and policymaking, DGI(2017)09, Strasbourg Cedex, Council of Europe Report, https://edoc.coe.int/en/media/7495-information-disorder-toward-an-interdisciplinary-framework-for-research-and-policy-making.html. Accessed 19 January 2025.
     [Google Scholar]
  42. Zhou, X., Zafarani, R., Shu, K. and Liu, H. (2019), ‘Fake news: fundamental theories, detection strategies and challenges’, WSDM ‘19, Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, Victoria, Australia, 11–15 February, New York: Association for Computing Machinery, pp. 836–37.
     [Google Scholar]
  43. Wang, Y., Yang, W., Ma, F., Xu, J., Zhong, B., Deng, Q. and Gao, J. (2020), ‘Weak supervision for fake news detection via reinforcement learning’, Proceedings of the AAAI Conference on Artificial Intelligence, 34:1, pp. 516–23, https://doi.org/10.1609/aaai.v34i01.5389.
     [Google Scholar]
  44. Zhou, X., Jain, A., Phoha, V. V. and Zafarani, R. (2019), ‘Fake news early detection: A theory-driven model’, Digital Threats: Research and Practice, 1:2, pp. 1–25, https://doi.org/10.1145/3377478.
     [Google Scholar]
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Detecting fake news: A hybrid approach combining convolutional neural networks with social learning theory
Intellect ; https://doi.org/10.1386/ajms_00199_1
/content/journals/10.1386/ajms_00199_1
/content/journals/10.1386/ajms_00199_1
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  • Article Type: Article
Keywords: social signals ; hybrid model ; convolutional neural networks ; social learning theory ; misinformation ; fake news detection

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