Early Detection of Fake News Using Multimodal Machine Learning

References

[1] Boididou, C., Papadopoulos, S., Zampoglou, M., Apostolidis, L., Papadopoulou, O., & Kompatsiaris, Y. (2018). Detection and verification of misinformation in social media. Multimedia Tools and Applications, 77(10), 12371–12404.

[2] Kaliyar, R. K., Goswami, A., & Narang, P. (2021). FNDNet: A deep convolutional neural network for fake news detection. Cognitive Systems Research, 61, 32–44.

[3] Khattar, D., Garg, A., & Varma, V. (2019). MVAE: Multimodal variational autoencoder for fake news detection. In Proceedings of the World Wide Web Conference (pp. 2915–2921).

[4] Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2020). Fake news detection on social media: A data mining perspective. ACM SIGKDD Explorations Newsletter, 19(1), 22–36.

[5] Shu, K., Wang, S., & Liu, H. (2019). Beyond news contents: The role of social context for fake news detection. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (pp. 312–320).

[6] Singh, V. K., Ghosh, S., & Jose, J. M. (2021). Toward multimodal fake news detection: A systematic review. Journal of the Association for Information Science and Technology, 72(12), 1606–1627.

[7] Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., & Gao, J. (2018). EANN: Event adversarial neural networks for multi-modal fake news detection. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 849–857).

[8] Wu, Y., Zhan, P., Zhang, Y., Wang, L., & Xu, Z. (2021). Multimodal fusion with co-attention for fake news detection. Neural Computing and Applications, 33, 18971–18984.

[9] Zhang, X., & Ghorbani, A. A. (2020). An overview of online fake news: Characterization, detection, and discussion. Information Processing & Management, 57(2), 102025.

[10] Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2022). Early detection of fake news on social media: A review. Information Processing & Management, 59(1), 102768.

@article{1717327,
      author = {Isha Pallavi Bara, Dr. Syed Shahid Raza},
      title = {Early Detection of Fake News Using Multimodal Machine Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {475-481},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717327.pdf},
      abstract = {The rapid growth of social media platforms has significantly increased the spread of fake news, posing serious threats to public trust, democratic processes and social harmony. Traditional fake news detection methods relying solely on textual analysis are often insufficient as modern misinformation is increasingly multimodal, combining text, images, videos and metadata to enhance credibility and emotional impact. This thesis proposes a multimodal machine learning framework for the early detection of fake news by jointly analysing textual, visual and contextual features. By integrating natural language processing techniques with deep visual feature extraction and multimodal fusion strategies the proposed approach aims to improve detection accuracy at early stages of news dissemination. Experimental evaluation on benchmark datasets demonstrates that multimodal models outperform unimodal approaches highlighting the importance of cross modal correlations in identifying deceptive content.},
      keywords = {Fake News Detection, Multimodal Machine Learning, Social Media Analysis, Deep Learning, Misinformation, Early Detection},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717327}
  }