Journal of Information and Knowledge (Formerly SRELS Journal of Information Management)

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Fake News Detection Using Hybrid Approach


Affiliations
1 Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Athwa, Surat - 395001, Gujarat, India
     

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Over the last few years, fake news has dramatically increased on social media. Fake news can originate from any number of sources and is shared across different social platforms. This type of information is used to spread for fun or economic gain. Our goal is to stop distributing this type of misleading information on social media or any other platform. In this paper, we have proposed a hybrid model (RoBERTa and BERT) to detect fake news. Our proposed architecture is based on the LIAR multi-label dataset. Our model shows promising results.

Keywords

BERT, Fake News, RoBERTa, Social Media
User
About The Authors

Him Gohil
Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Athwa, Surat - 395001, Gujarat
India

Vandana Joshi
Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Athwa, Surat - 395001, Gujarat
India

Snehal Gandhi
Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Athwa, Surat - 395001, Gujarat
India


Notifications

  • Devlin, J., Chang, M.-W., Lee, K., Google, K. T., & Language, A. I. BERT: Pre-training of deep bidirectional transformers for language understanding. https://github.com/tensorflow/ tensor2tensor

  • Goldani, M. H., Momtazi, S., & Safabakhsh, R. (2021). Detecting fake news with capsule neural networks. Applied Soft Computing, 101, 106991. https://doi.org/10.1016/j. asoc.2020.106991

  • Jadhav, S. S., & Thepade, S. D. (2019). Fake news identification and classification using DSSM and improved recurrent neural network classifier. Applied Artificial Intelligence, 1–11. https://doi.org/10.1080/08839514.2019.1661579

  • Li, Y., Jiang, B., Shu, K., & Liu, H. (2020). Toward a multilingual and multimodal data repository for COVID- 19 disinformation. International Conference on Big Data. https://doi.org/10.1109/bigdata50022.2020.9378472

  • Liu, J., Wang, C., Li, C., Li, N., Deng, J., & Pan, J. Z. (2021). DTN: Deep triple network for topic specific fake news detection. Journal of Web Semantics, 100646. https://doi. org/10.1016/j.websem.2021.100646

  • Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M. S., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A robustly optimized BERT pretraining approach. ArXiv (Cornell University). https://doi.org/10.48550/arxiv.1907.11692

  • Nagoudi, E. B., Elmadany, A. R., Abdul-Mageed, M., Alhindi, T., & Cavusoglu, H. Machine generation and detection of Arabic manipulated and fake news. (2020). Proceedings of the Fourth Arabic Natural Language Processing Workshop, Barcelona, Spain.

  • Nasir, J. A., Khan, O. S., & Varlamis, I. (2021). Fake news detection: A hybrid CNN-RNN based deep learning approach. International Journal of Information Management Data Insights, 1(1), 100007. https://doi.org/10.1016/j. jjimei.2020.100007

  • Paka, W. S., Bansal, R., Kaushik, A., Sengupta, S., & Chakraborty, T. (2021). Cross-SEAN: A cross-stitch semisupervised neural attention model for COVID-19 fake news detection. Applied Soft Computing, 107, 107393. https://doi.org/10.1016/j.asoc.2021.107393 PMid:36568256 PMCid:PMC9761197

  • Probierz, B., Stefański, P., & Kozak, J. (2021). Rapid detection of fake news based on machine learning methods. Procedia Computer Science, 192, 2893–2902. https://doi. org/10.1016/j.procs.2021.09.060

  • Shim, J.-S., Lee, Y., & Ahn, H. (2021). A link2vec-based fake news detection model using web search results. Expert Systems with Applications, 184, 115491. https://doi. org/10.1016/j.eswa.2021.115491

  • Wang, W. Y. (n.d.). Liar, liar pants on fire: A new benchmark dataset for fake news detection. https://www.cs.ucsb.edu/


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  • Fake News Detection Using Hybrid Approach

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Authors

Him Gohil
Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Athwa, Surat - 395001, Gujarat, India
Vandana Joshi
Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Athwa, Surat - 395001, Gujarat, India
Snehal Gandhi
Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Athwalines, Athwa, Surat - 395001, Gujarat, India

Abstract


Over the last few years, fake news has dramatically increased on social media. Fake news can originate from any number of sources and is shared across different social platforms. This type of information is used to spread for fun or economic gain. Our goal is to stop distributing this type of misleading information on social media or any other platform. In this paper, we have proposed a hybrid model (RoBERTa and BERT) to detect fake news. Our proposed architecture is based on the LIAR multi-label dataset. Our model shows promising results.

Keywords


BERT, Fake News, RoBERTa, Social Media

References





DOI: https://doi.org/10.17821/srels%2F2024%2Fv61i2%2F171046