An Ensemble Classification and Hybrid Feature Selection Approach for Fake News Stance Detection
Article Sidebar
Main Article Content
Abstract
The developments in Internet and notions of social media have revolutionised representations and disseminations of news. News spreads quickly while costing less in social media. Amidst these quick distributions, dangerous or seductive information like user generated false news also spread equally. on social media. Distinguishing true incidents from false news strips create key challenges. Prior to sending the feature vectors to the classifier, it was suggested in this study effort to use dimensionality reduction approaches to do so. These methods would not significantly affect the result, though. Furthermore, utilising dimensionality reduction techniques significantly reduces the time needed to complete a forecast. This paper presents a hybrid feature selection method to overcome the above mentioned issues. The classifications of fake news are based on ensembles which identify connections between stories and headlines of news items. Initially, data is pre-processed to transform unstructured data into structures for ease of processing. In the second step, unidentified qualities of false news from diverse connections amongst news articles are extracted utilising PCA (Principal Component Analysis). For the feature reduction procedure, the third step uses FPSO (Fuzzy Particle Swarm Optimization) to select features. To efficiently understand how news items are represented and spot bogus news, this study creates ELMs (Ensemble Learning Models). This study obtained a dataset from Kaggle to create the reasoning. In this study, four assessment metrics have been used to evaluate performances of classifying models.
Article Details
References
Manzoor, S. I., & Singla, J. (2019, April). Fake news detection using machine learning approaches: A systematic review. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 230-234). IEEE.
Gilda, S. (2017, December). Notice of Violation of IEEE Publication Principles: Evaluating machine learning algorithms for fake news detection. In 2017 IEEE 15th student conference on research and development (SCOReD) (pp. 110-115). IEEE.
Khan, J. Y., Khondaker, M., Islam, T., Iqbal, A., & Afroz, S. (2019). A benchmark study on machine learning methods for fake news detection. arXiv preprint arXiv:1905.04749.
Zhou, X., Zafarani, R., Shu, K., & Liu, H. (2019, January). Fake news: Fundamental theories, detection strategies and challenges. In Proceedings of the twelfth ACM international conference on web search and data mining (pp. 836-837).
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.
Zhou, X., & Zafarani, R. (2018). Fake news: A survey of research, detection methods, and opportunities. arXiv preprint arXiv:1812.00315, 2.
Oshikawa, R., Qian, J., & Wang, W. Y. (2018). A survey on natural language processing for fake news detection. arXiv preprint arXiv:1811.00770.
Parikh, S. B., & Atrey, P. K. (2018, April). Media-rich fake news detection: A survey. In 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 436-441). IEEE.
Bondielli, A., & Marcelloni, F. (2019). A survey on fake news and rumour detection techniques. Information Sciences, 497, 38-55.
Elhadad, M. K., Li, K. F., & Gebali, F. (2019, August). Fake news detection on social media: a systematic survey. In 2019 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) (pp. 1-8). IEEE.
Lillie, A. E., & Middelboe, E. R. (2019). Fake news detection using stance classification: A survey. arXiv preprint arXiv:1907.00181.
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.
Reis, J. C., Correia, A., Murai, F., Veloso, A., & Benevenuto, F. (2019). Supervised learning for fake news detection. IEEE Intelligent Systems, 34(2), 76-81.
Ahmed, H., Traore, I., & Saad, S. (2017, October). Detection of online fake news using n-gram analysis and machine learning techniques. In International conference on intelligent, secure, and dependable systems in distributed and cloud environments (pp. 127-138). Springer, Cham.
R?descu, R. (2020, July). AUTOMATIC ADAPTIVE SYSTEM FOR FILTERING FAKE NEWS BY METHODS BASED ON ARTIFICIAL INTELLIGENCE USING MACHINE LEARNING. In Proceedings of EDULEARN20 Conference (Vol. 6, p. 7th).
Rubin, V. L., Chen, Y., & Conroy, N. K. (2015). Deception detection for news: three types of fakes. Proceedings of the Association for Information Science and Technology, 52(1), 1-4.
Ruchansky, N., Seo, S., & Liu, Y. (2017, November). Csi: A hybrid deep model for fake news detection. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (pp. 797-806).
Tacchini, E., Ballarin, G., Della Vedova, M. L., Moret, S., & de Alfaro, L. (2017). Some like it hoax: Automated fake news detection in social networks. arXiv preprint arXiv:1704.07506.
Pérez-Rosas, V., Kleinberg, B., Lefevre, A., & Mihalcea, R. (2017). Automatic detection of fake news. arXiv preprint arXiv:1708.07104.
Saikh, T., De, A., Ekbal, A., & Bhattacharyya, P. (2020). A deep learning approach for automatic detection of fake news. arXiv preprint arXiv:2005.04938.
Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B. (2017). A stylometric inquiry into hyperpartisan and fake news. arXiv preprint arXiv:1702.05638.
Umer, M., Imtiaz, Z., Ullah, S., Mehmood, A., Choi, G. S., & On, B. W. (2020). Fake news stance detection using deep learning architecture (CNN-LSTM). IEEE Access, 8, 156695-156706.
D. Pomerleau and Rao. (2017). Fake News Challenge Dataset. Accessed: Oct. 29, 2019. [Online]. Available: http://www.fakenewschallenge.org/.
S. Deegalla and H. Bostrom, ``Reducing high-dimensional data by principal component analysis vs. Random projection for nearest neighbor classification,'' in Proc. 5th Int. Conf. Mach. Learn. Appl. (ICMLA), Dec. 2006, pp. 245_250.
D. Hou, J. Zhang, Z. Yang, S. Liu, P. Huang, and G. Zhang, ``Distribution water quality anomaly detection from UV optical sensor monitoring data by integrating principal component analysis with chisquare distribution,'' Opt. Express, vol. 23, no. 13, p. 17487, Jun. 2015, doi: 10.1364/OE.23.017487.
Bai, Q. (2010). Analysis of particle swarm optimization algorithm. Computer and information science, 3(1), 180.
Lingras, P., & Jensen, R. (2007, July). Survey of rough and fuzzy hybridization. In 2007 IEEE International Fuzzy Systems Conference (pp. 1-6). IEEE.
Ren, G., Cao, Y., Wen, S., Huang, T., & Zeng, Z. (2018). A modified Elman neural network with a new learning rate scheme. Neurocomputing, 286, 11-18.
Song, M., & Pedrycz, W. (2013). Granular neural networks: concepts and development schemes. IEEE transactions on neural networks and learning systems, 24(4), 542-553.
Pedrycz, W., & Vukovich, G. (2001). Granular neural networks. Neurocomputing, 36(1-4), 205-224.
Kobayashi, K., & Komaki, F. (2006). Information criteria for support vector machines. IEEE transactions on neural networks, 17(3), 571-577.
Chung, K. M., Kao, W. C., Sun, C. L., Wang, L. L., & Lin, C. J. (2003). Radius margin bounds for support vector machines with the RBF kernel. Neural computation, 15(11), 2643-2681.