Detecting and Mitigating the Dissemination of Fake News : Challenges and Future Research Opportunities

Authors

  • Bathini Pravalika  Associate Professor & Vice Principal, Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India
  • Amati Sanghavi  Student, Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India
  • Yasmeen  Student, Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India

Keywords:

Social Media, News Consumption, Fake News, Machine Learning Algorithms.

Abstract

With increasing popularity in the use of social media for news consumption, the substantial widespread dissemination of fake news has the potential to adversely affect individuals as well as the society as a whole. Even in the midst of the current covid-19 pandemic, false information shared on websites such as WhatsApp, Twitter, and Facebook have the potential to cause panic and shock a large number of people in various parts of the world. These misconceptions obscure healthier habits and encourage incorrect procedures, which aid in the transmission of the virus and, as a result, result in poor physical and psychological health results for individuals. Therefore, it is a research challenge to validate the source, content and publisher of a news article for classifying it as genuine or fake. The existing systems and techniques are not efficient enough to accurately classify a given news based on its statistical rating. Machine learning plays an imperative part in categorizing news data and information, despite some limitations. Our project not only aims on fake news detection but also on generation of real news once the fake news is detected. We propose a user-friendly webpage on which the user enters the news article statement. It is then tested by our machine learning algorithm which then classifies it as genuine or fake, after which the important words are extracted from the statement which helps to get the corresponding genuine news by scraping it from trusted sources and show it to the user. We have compared two machine learning algorithms in this which are- Passive Aggressive Classifier and Naïve Bayes algorithm. We got an accuracy of about 93.5% from Passive Aggressive Classifier and about 83.5% from Naïve Bayes algorithm.

References

  1. Yang, S., Shu, K., Wang, S., Gu, R., Wu, F. and Liu, H., 2019, July. Unsupervised fake news detection on social media: A generative approach. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 5644-5651).
  2. Kai Shu , Amy Sliva , Suhang Wang , Jiliang Tang , and Huan Liu,2017 september. Fake News Detection on Social Media: A Data Mining Perspective
  3. Yanagi, Y., Orihara, R., Sei, Y., Tahara, Y. and Ohsuga, A., 2020, July. Fake News Detection with Generated Comments for News Articles. In 2020 IEEE 24th International Conference on Intelligent Engineering Systems (INES) (pp. 85-90). IEEE.
  4. Zhang, J., Dong, B. and Philip, S.Y., 2020, April. Fakedetector: Effective fake news detection with deep diffusive neural network. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1826-1829). IEEE.
  5. Thota, A., Tilak, P., Ahluwalia, S. and Lohia, N., 2018. Fake news detection: A deep learning approach. SMU Data Science Review, 1(3), p.10.
  6. Shu, K., Mahudeswaran, D., Wang, S., Lee, D. and Liu, H., 2020. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media. Big Data, 8(3), pp.171-188.
  7. Groza, A., 2020. Detecting fake news for the new coronavirus by reasoning on the Covid-19 ontology. arXiv preprint arXiv:2004.12330.
  8. Gurav, S., Sase, S., Shinde, S., Wabale, P. and Hirve, S., 2019. Survey on Automated System for Fake News Detection using NLP & Machine Learning Approach. International Research Journal of Engineering and Technology (IRJET), 6(01), pp.308-309.
  9. Yang, K.C., Niven, T. and Kao, H.Y., 2019. Fake news detection as natural language inference. arXiv preprint arXiv:1907.07347.
  10. Cui, L. and Lee, D., 2020. Coaid: Covid-19 healthcare misinformation dataset. ArXiv preprint arXiv:2006.00885.
  11. Qi, P., Cao, J., Yang, T., Guo, J. and Li, J., 2019, November. Exploiting multi-domain visual information for fake news detection. In 2019 IEEE International Conference on Data Mining (ICDM) (pp. 518-527). IEEE.
  12. Srivastava, A., Kannan, R., Chelmis, C. and Prasanna, V.K., 2019, December. RecANt: Network-based Recruitment for Active Fake News Correction. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 940-949). IEEE
  13. Long, Y., 2017. Fake news detection through multi-perspective speaker profiles. Association for Computational Linguistics.
  14. Wang, W. Y. 2017. ‖ liar, liar pants on fire‖: A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648
  15. Jin, Z.; Cao, J.; Zhang, Y.; and Luo, J. 2016. News verification by exploiting conflicting social viewpoints in microblogs. In AAAI, 2972–2978.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-06-30

Issue

Volume 9, Issue 3, May-June-2023

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Bathini Pravalika, Amati Sanghavi, Yasmeen, " Detecting and Mitigating the Dissemination of Fake News : Challenges and Future Research Opportunities" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.361-367, May-June-2023.