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Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
Research Article | Published: 12 December 2024
IECE Transactions on Intelligent Systematics Volume 1, Issue 3, 2024: 203-214
Volume 1, Issue 3, IECE Transactions on Intelligent Systematics
Volume 1, Issue 3, 2024
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Inam Ullah
Inam Ullah
Gachon University, Republic of Korea
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IECE Transactions on Intelligent Systematics, Volume 1, Issue 3, 2024: 203-214

Free to Read | Research Article | 12 December 2024
Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
by
Muhammad Nadeem Muhammad Nadeem 1,3
Parchamdar Abbas Parchamdar Abbas 2
Wei Zhang Wei Zhang 1,3 *
Sumaira Rafique Sumaira Rafique 4 *
Sundas Iqbal Sundas Iqbal 5
1 College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
2 Department of Computer Science, National University of Modern Languages (NUML), Islamabad, Pakistan
3 Faculty of Information technology and Engineering, Ocean University of China, Qingdao 266100, China
4 National University of Computer and Emerging Sciences sub Campus Milad street, Faisal Town Lahore, Pakistan
5 School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China
* Corresponding Authors: Wei Zhang, [email protected] ; Sumaira Rafique, [email protected]
DOI: 10.62762/TIS.2024.461943
Received: 13 November 2024, Accepted: 05 December 2024, Published: 12 December 2024  
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Abstract
The increasing prevalence of fake news on social media has become a significant challenge in today’s digital landscape. This paper proposes a hybrid framework for fake news detection, combining Natural Language Processing (NLP) techniques and machine learning algorithms. Using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction, and classifiers such as Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Machines (SVM), the model integrates Maximum Likelihood Estimation (MLE) with Logistic Regression to achieve 95% accuracy and 93% precision on a Kaggle dataset. The results highlight the potential of combining statistical and NLP approaches to improve fake news detection accuracy.
Graphical Abstract
Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework
Keywords
fake news
natural language processing
statistical technique
machine learning
maximum likelihood estimation
social media
Funding
This work was supported without any funding.
References
  1. Ahmed, M. J., Afridi, U., Shah, H. A., Khan, H., Bhatt, M. W., Alwabli, A., & Ullah, I. (2024). CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems. SLAS technology, 29(5), 100193.
    [Google Scholar]
  2. Posetti, J., & Matthews, A. (2018). A short guide to the history of ‘fake news’ and disinformation. International Center for Journalists, 7(2018), 2018-07.
    [Google Scholar]
  3. Tschiatschek, S., Singla, A., Gomez Rodriguez, M., Merchant, A., & Krause, A. (2018, April). Fake news detection in social networks via crowd signals. In Companion proceedings of the the web conference 2018 (pp. 517-524).
    [Google Scholar]
  4. Khan, H., Ullah, I., Shabaz, M., Omer, M. F., Usman, M. T., Guellil, M. S., & Koo, J. (2024). Visionary vigilance: Optimized YOLOV8 for fallen person detection with large-scale benchmark dataset. Image and Vision Computing, 149, 105195.
    [Google Scholar]
  5. Mattos, D. M. F., Velloso, P. B., & Duarte, O. C. M. B. (2019). An agile and effective network function virtualization infrastructure for the Internet of Things. Journal of Internet Services and Applications, 10(1), 6.
    [Google Scholar]
  6. Ribeiro, F. N., Saha, K., Babaei, M., Henrique, L., Messias, J., Benevenuto, F., ... & Redmiles, E. M. (2019, January). On microtargeting socially divisive ads: A case study of russia-linked ad campaigns on facebook. In Proceedings of the conference on fairness, accountability, and transparency (pp. 140-149).
    [Google Scholar]
  7. Usman, M. T., Khan, H., Singh, S. K., Lee, M. Y., & Koo, J. (2024). Efficient deepfake detection via layer-frozen assisted dual attention network for consumer imaging devices. IEEE Transactions on Consumer Electronics.
    [Google Scholar]
  8. Tardáguila, C., Benevenuto, F., & Ortellado, P. (2018). Fake News Is Poisoning Brazilian Politics. WhatsApp Can Stop It. International New York Times, NA-NA.
    [Google Scholar]
  9. Ullah, I., Ali, F., Khan, H., Khan, F., & Bai, X. (2024). Ubiquitous computation in internet of vehicles for human-centric transport systems. Computers in Human Behavior, 161, 108394.
    [Google Scholar]
  10. Zhou, X., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys (CSUR), 53(5), 1-40.
    [Google Scholar]
  11. Wang, W. Y. (2017). “ liar, liar pants on fire”: A new benchmark dataset for fake news detection. arXiv preprint arXiv:1705.00648.
    [Google Scholar]
  12. Rubin, V. L. (2010). On deception and deception detection: Content analysis of computer-mediated stated beliefs. Proceedings of the American Society for Information Science and Technology, 47(1), 1-10.
    [Google Scholar]
  13. Rubin, V. L., Conroy, N., Chen, Y., & Cornwell, S. (2016, June). Fake news or truth? using satirical cues to detect potentially misleading news. In Proceedings of the second workshop on computational approaches to deception detection (pp. 7-17).
    [Google Scholar]
  14. Gottfried, J., & Shearer, E. (2016). News use across social media platforms 2016.
    [Google Scholar]
  15. Campan, A., Cuzzocrea, A., & Truta, T. M. (2017, December). Fighting fake news spread in online social networks: Actual trends and future research directions. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 4453-4457). IEEE.
    [Google Scholar]
  16. Lazer, D. M., Baum, M. A., Benkler, Y., Berinsky, A. J., Greenhill, K. M., Menczer, F., ... & Zittrain, J. L. (2018). The science of fake news. Science, 359(6380), 1094-1096.
    [Google Scholar]
  17. Chadwick, A., & Vaccari, C. (2019). News sharing on UK social media: Misinformation, disinformation, and correction.
    [Google Scholar]
  18. Kogan, S., Moskowitz, T. J., & Niessner, M. (2019). Fake news: Evidence from financial markets. Available at SSRN, 3237763.
    [Google Scholar]
  19. Zafarani, R., Zhou, X., Shu, K., & Liu, H. (2019, July). Fake news research: Theories, detection strategies, and open problems. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 3207-3208).
    [Google Scholar]
  20. Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-236.
    [Google Scholar]
  21. Thompson, S. A. (2017, December 14). President Trump’s lies, the definitive list. The New York Times - Breaking News, US News, World News and Videos. https://www.nytimes.com/interactive/2017/06/23/ opinion/trumps-lies.html
    [Google Scholar]
  22. Ur Rehman, I., Ullah, I., Khan, H., Guellil, M. S., Koo, J., Min, J., ... & Lee, M. Y. (2024). A comprehensive systematic literature review of ML in nanotechnology for sustainable development. Nanotechnology Reviews, 13(1), 20240069.
    [Google Scholar]
  23. Graauwmans, V. V. (2016). Fake News in the Online World: An Experimental Study on Credibility Evaluations of Fake News depending on Information Processing Bachelor Thesis Tilburg University.
    [Google Scholar]
  24. Guderlei, M., & Aßenmacher, M. (2020, December). Evaluating unsupervised representation learning for detecting stances of fake news. In Proceedings of the 28th international conference on computational linguistics (pp. 6339-6349).
    [Google Scholar]
  25. Khan, H., Usman, M. T., Rida, I., & Koo, J. (2024). Attention enhanced machine instinctive vision with human-inspired saliency detection. Image and Vision Computing, 152, 105308.
    [Google Scholar]
  26. Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017). Fake news detection on social media: A data mining perspective. ACM SIGKDD explorations newsletter, 19(1), 22-36.
    [Google Scholar]
  27. Anderson, J. (1983). Lix and rix: Variations on a little-known readability index. Journal of Reading, 26(6), 490-496.
    [Google Scholar]
  28. Amorim, E., Cançado, M., & Veloso, A. (2018, June). Automated essay scoring in the presence of biased ratings. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 229-237).
    [Google Scholar]
  29. An, J., & Weber, I. (2016). # greysanatomy vs.# yankees: Demographics and Hashtag Use on Twitter. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 10, No. 1, pp. 523-526).
    [Google Scholar]
  30. Ahmed, H., Traore, I., & Saad, S. (2017). Detection of online fake news using n-gram analysis and machine learning techniques. In Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments: First International Conference, ISDDC 2017, Vancouver, BC, Canada, October 26-28, 2017, Proceedings 1 (pp. 127-138). Springer International Publishing.
    [Google Scholar]
  31. An, J., & Kwak, H. (2017, May). What gets media attention and how media attention evolves over time: large-scale empirical evidence from 196 countries. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 11, No. 1, pp. 464-467).
    [Google Scholar]
  32. Srivastava, A. (2020). Real time fake news detection using machine learning and NLP. Int. Res. J. Eng. Technol.(IRJET), 7(06).
    [Google Scholar]
  33. Khan, H., Jan, Z., Ullah, I., Alwabli, A., Alharbi, F., Habib, S., ... & Koo, J. (2024). A deep dive into AI integration and advanced nanobiosensor technologies for enhanced bacterial infection monitoring. Nanotechnology Reviews, 13(1), 20240056.
    [Google Scholar]
  34. Lakshmanarao, A., Swathi, Y., & Kiran, T. S. R. (2019). An effecient fake news detection system using machine learning. International Journal of Innovative Technology and Exploring Engineering, 8(10), 3125-3129.
    [Google Scholar]
  35. Hiramath, C. K., & Deshpande, G. C. (2019, July). Fake news detection using deep learning techniques. In 2019 1st International Conference on Advances in Information Technology (ICAIT) (pp. 411-415). IEEE.
    [Google Scholar]
  36. Mahir, E. M., Akhter, S., & Huq, M. R. (2019, June). Detecting fake news using machine learning and deep learning algorithms. In 2019 7th international conference on smart computing & communications (ICSCC) (pp. 1-5). IEEE.
    [Google Scholar]
  37. Granik, M., & Mesyura, V. (2017, May). Fake news detection using naive Bayes classifier. In 2017 IEEE first Ukraine conference on electrical and computer engineering (UKRCON) (pp. 900-903). IEEE.
    [Google Scholar]
  38. Ali, D., Iqbal, S., Mehmood, S., Khalil, I., Ullah, I., Khan, H., & Ali, F. (2024). Unleashing the Power of AI in Communication Technology: Advances, Challenges, and Collaborative Prospects. In Artificial General Intelligence (AGI) Security: Smart Applications and Sustainable Technologies (pp. 211-226). Singapore: Springer Nature Singapore.
    [Google Scholar]
  39. Gadekar, P. S. (2019). Fake News Identification using Machine Learning. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 7(V).
    [Google Scholar]
  40. Ivancová, K., Sarnovský, M., & Maslej-Krcšñáková, V. (2021, January). Fake news detection in Slovak language using deep learning techniques. In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000255-000260). IEEE.
    [Google Scholar]
  41. Meesad, P. (2021). Thai fake news detection based on information retrieval, natural language processing and machine learning. SN Computer Science, 2(6), 425.
    [Google Scholar]
  42. Cai, Y., Pan, S., Wang, X., Chen, H., Cai, X., & Zuo, M. (2020). Measuring distance-based semantic similarity using meronymy and hyponymy relations. Neural Computing and Applications, 32, 3521-3534.
    [Google Scholar]
  43. Bali, A. P. S., Fernandes, M., Choubey, S., & Goel, M. (2019). Comparative performance of machine learning algorithms for fake news detection. In Advances in Computing and Data Sciences: Third International Conference, ICACDS 2019, Ghaziabad, India, April 12–13, 2019, Revised Selected Papers, Part II 3 (pp. 420-430). Springer Singapore.
    [Google Scholar]
  44. Ali, D., Huque, M. T., Godhuli, J. J., & Ahmed, N. (2022). Detection of Face Emotion and Music Recommendation System using Machine Learning. International Journal of Research and Innovation in Applied Science, 7(11), 05-08.
    [Google Scholar]
  45. Bangyal, W. H., Qasim, R., Rehman, N. U., Ahmad, Z., Dar, H., Rukhsar, L., ... & Ahmad, J. (2021). Detection of Fake News Text Classification on COVID-19 Using Deep Learning Approaches. Computational and mathematical methods in medicine, 2021(1), 5514220.
    [Google Scholar]
  46. Wu, J., Huang, C., & Chen, Y. (2020, October). Patent Text Classification Study Based on Bi-LSTM-A Model. In 2020 5th international conference on control, Robotics and Cybernetics (CRC) (pp. 1-5). IEEE.
    [Google Scholar]
  47. Ganesh, P., Priya, L., & Nandakumar, R. (2021, June). Fake news detection-a comparative study of advanced ensemble approaches. In 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 1003-1008). IEEE.
    [Google Scholar]
Cite This Article
APA Style
Nadeem, M., Abbas, P., Zhang, W., Rafique, S., & Iqbal, S. (2024). Enhancing Fake News Detection with a Hybrid NLP-Machine Learning Framework. IECE Transactions on Intelligent Systematics, 1(3), 203–214. https://doi.org/10.62762/TIS.2024.461943
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