BharatFakeNewsTracker: An Ensemble Learning Tool to Spot Fake News in India
BharatFakeNewsTracker: An Ensemble Learning Tool to Spot Fake News in India |
||
 |
 |
|
| © 2023 by IJETT Journal | ||
| Volume-71 Issue-9 |
||
| Year of Publication : 2023 | ||
| Author : Manish Kumar Singh, Jawed Ahmed, Kamlesh Kumar Raghuvanshi, Mohammad Afshar Alam |
||
| DOI : 10.14445/22315381/IJETT-V71I9P208 | ||
How to Cite?
Manish Kumar Singh, Jawed Ahmed, Kamlesh Kumar Raghuvanshi, Mohammad Afshar Alam, "BharatFakeNewsTracker: An Ensemble Learning Tool to Spot Fake News in India," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 76-91, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P208
Abstract
In recent years, the proliferation of fake news has grown to be a significant worry in India, posing some danger to democracy, social stability, and public trust. To address this challenge, the current paper proposes BharatFakeNewsTracker, a machine learning-based ensemble framework combining multiple algorithms to identify fake news occurrences in India automatically. Several experiments are carried out on a large-scale dataset, BharatFakeNewsKosh, to assess how well the proposed model performs, containing Indian fake news events. The experiment findings demonstrate that the suggested framework attains great accuracy in identifying 94%, outperforming several baselines and state-of-the-art methods. Furthermore, the paper investigates the impact of different hyperparameters and model configurations on detection performance and proves the suggested work's robustness and effectiveness across myriad settings. The current study contributes to developing reliable and scalable tools for detecting fake news in India. It highlights how ensemble machine-learning techniques are capable of addressing this complex problem.
Keywords
Indian fake news, Ensemble learning, BharatFakeNewsKosh, Adversarial attack, Information credibility.
References
[1] Kai Shu et al., “Fake News Detection on Social Media: A Data Mining Perspective,” ACM SIGKDD Explorations Newsletter, vol. 19, no. 1, pp. 22-36, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[2] João Pedro Baptista, and Anabela Gradim, “Understanding Fake News Consumption: A Review,” Social Sciences, vol. 9, no. 10, p. 185, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Xinyi Zhou, and Reza Zafarani, “A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities,” ACM Computing Surveys, vol. 53, no. 5, pp. 1-40, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Xiang Zhang, Junbo Zhao, and Yann LeCun, “Character-Level Convolutional Networks for Text Classification,” Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 649-657, 2015.
[Google Scholar] [Publisher Link]
[5] Martin Potthast et al., “A Stylometric Inquiry into Hyperpartisan and Fake News,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 231-240, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Soroush Vosoughi, Deb Roy, and Sinan Aral, “The Spread of True and False News Online,” Science, vol. 359, no. 6380, pp. 1146-1151, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Jieun Shin et al., “The Diffusion of Misinformation on Social Media: Temporal Pattern, Message, and Source”, Computers in Human Behavior, vol. 83, pp. 278-287, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Carlos Castillo, Marcelo Mendoza and Barbara Poblete, “Information Credibility on Twitter,” Proceedings of the 20th International Conference on World Wide Web, pp. 675-684, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[9] William Yang Wang, “Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection,” Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, vol. 2, pp. 422-426, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Aswini Thota et al., “Fake News Detection: A Deep Learning Approach,” SMU Data Science Review, vol. 1, no. 3, 2018.
[Google Scholar] [Publisher Link]
[11] Natali Ruchansky et al., “CSI: A Hybrid Deep Model for Fake News Detection,” Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797-806, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Oluwaseun Ajao, Deepayan Bhowmik, and Shahrzad Zargari, “Fake News Identification on Twitter with Hybrid CNN and RNN Models,” Proceedings of the 9th International Conference on Social Media and Society, pp. 226-230, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Iftikhar Ahmad et al., “Fake News Detection Using Machine Learning Ensemble Methods,” Complexity, vol. 2020, pp. 1-11, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Rohit Kumar Kaliyar, Anurag Goswami, and Pratik Narang, “Multiclass Fake News Detection Using Ensemble Machine Learning,” IEEE 9th International Conference on Advanced Computing (IACC) IEEE, pp. 103-107, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Yin-Fu Huang, and Po-Hong Chen, “Fake News Detection Using an Ensemble Learning Model Based on Self-Adaptive Harmony Search Algorithms,” Expert Systems with Applications, vol. 159, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Arush Agarwal, and Akhil Dixit, “Fake News Detection: An Ensemble Learning Approach,” 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE, pp. 1178-1183, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Bharat Fake News Kosh. [online]. Available: https://bharatfakenewskosh.com/datasets/
[18] Mauik Panchal, and Rutika Ghariya, “A Review On Detection of Fake News Using Various Techniques,” SSRG International Journal of Computer Science and Engineering, vol. 8, no. 6, pp. 1-4, 2021.
[CrossRef] [Publisher Link]
[19] Jair Cervantes et al., “A Comprehensive Survey on Support Vector Machine Classification: Applications, Challenges and Trends,” Neurocomputing, vol. 408, pp. 189-215, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Derek A. Pisner, and David M. Schnyer, “Support Vector Machine,” Machine Learning, pp. 101-121, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Liaqat Ali et al., “An Optimized Stacked Support Vector Machines Based Expert System for the Effective Prediction of Heart Failure,” IEEE Access, vol. 7, pp. 54007-54014, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Matthias Schonlau, and Rosie Yuyan Zou, “The Random Forest Algorithm for Statistical Learning,” The Stata Journal, vol. 20, no. 1, pp. 3-29, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Anjaneyulu Babu Shaik, and Sujatha Srinivasan, “A Brief Survey on Random Forest Ensembles in Classification Model,” International Conference on Innovative Computing and Communications: Proceedings of ICICC 2018, vol. 2, pp. 253-260, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Mumtaz Ali et al., “Complete Ensemble Empirical Mode Decomposition Hybridized with Random Forest and Kernel Ridge Regression Model for Monthly Rainfall Forecasts,” Journal of Hydrology, vol. 584, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Ekaba Bisong, “Logistic Regression,” Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 243-250, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Tae-Hwy Lee, Aman Ullah, and Ran Wang, “Bootstrap Aggregating and Random Forest,” Macroeconomic Forecasting in the Era of Big Data, pp. 389-429, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Raveendranadh Bokka, and Tamilselvan Sadasivam, “Securing IoT Networks: RPL Attack Detection with Deep Learning GRU Networks,” International Journal of Recent Engineering Science, vol. 10, no. 2, pp. 13-21, 2023.
[CrossRef] [Publisher Link]
[28] Tianqi Chen, and Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System,” Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Hui Jiang et al., “Network Intrusion Detection Based on PSO-XGBoost Model,” IEEE Access, vol. 8, pp. 58392-58401, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Rikiya Yamashita et al., “Convolutional Neural Networks: An Overview and Application in Radiology,” Insights into Imaging, vol. 9, pp. 611-629, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Varad A. Sarve, Swati S. Sherekar and Vilas M. Thakare, “Learning the Channel Uncertainty for Defensive Security Enhancements in MANET with Trust Management,” SSRG International Journal of Mobile Computing and Application, vol. 4, no. 1, pp. 21-27, 2017.
[CrossRef] [Publisher Link]
[32] Ilya Sutskever, James Martens, and Geoffrey Hinton, “Generating Text with Recurrent Neural Networks,” Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 1017-1024, 2011.
[Google Scholar]