A Survey on Detection of Fake and Biased News using Machine Learning Techniques

Autor: Dr Deepak N R, Akshaya R, Anju Krishnan R, Archana Krishnan R, Arya Prasad, Tabassum Ara
Jazyk: angličtina
Rok vydání: 2021
Předmět:
DOI: 10.5281/zenodo.5406872
Popis: Fake news is exponentially grown due to the popularity and ease of putting forward our thoughts and opinion. This easily can be believed by a big mass of people and circulating the same causes chaos and unwanted disputes. With fierce competition in news outlets and social media, it has become a threat to consumers. For months, the Corona virus and its associated impact has given rise to many fake news worldwide, including India. During the initial stages of the corona, fake news particularly on social media was spread recklessly. Contents and news about the origin, cause, symptoms, and cure and conspiracy theories about the virus were on the feed. This brings in the need to help the masses to be able to not fall prey to this fake or biased news using ML algorithms. The use of ensemble machine learning algorithms and vectorizers and the main theme of this paper.
{"references":["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.","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.","de Oliveira, N. R., Medeiros, D. S., & Mattos, D. M. (2020). A sensitive stylistic approach to identify fake news on social networking. IEEE Signal Processing Letters, 27, 1250-1254.","De Beer, D., & Matthee, M. (2020, May). Approaches to identify fake news: a systematic literature review. In International Conference on Integrated Science (pp. 13-22). Springer, Cham.","Sansonetti, G., Gasparetti, F., D'aniello, G., & Micarelli, A. (2020). Unreliable Users Detection in Social Media: Deep Learning Techniques for Automatic Detection. IEEE Access, 8, 213154-213167.","Thota, A., Tilak, P., Ahluwalia, S., & Lohia, N. (2018). Fake news detection: a deep learning approach. SMU Data Science Review, 1(3), 10.11","Elhadad, M. K., Li, K. F., & Gebali, F. (2020). Detecting misleading information on covid-19. Ieee Access, 8, 165201-165215.","Reddy, H., Raj, N., Gala, M., & Basava, A. (2020). Text-mining-based fake news detection using ensemble methods. International Journal of Automation and Computing, 17(2), 210-221.","Nyow, N. X., & Chua, H. N. (2019, November). Detecting fake news with tweets' properties. In 2019 IEEE Conference on Application, Information and Network Security (AINS) (pp. 24-29). IEEE.","Michael Muhlmeyer, Shaurya Agarwal, Member IEEE and Jiheng Huang, \"Modeling Social Contagion and Information Diffusion in Complex Socio-Technical Systems"]}
Databáze: OpenAIRE
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