Open Access
Subscription Access
An Overview on Identification of Phony and One-sided News Utilizing AI Strategies
Abstract
Counterfeit news is dramatically become because of the ubiquity and simplicity of advancing our considerations and assessment. This effectively can be trusted by a major mass of individuals and circling similar causes turmoil and undesirable questions. With furious rivalry in media sources and virtual entertainment, it has turned into a danger to shoppers. For quite a long time, the Covid and its related effect has led to many phony news around the world, including India. During the underlying phases of the crown, counterfeit news especially via online entertainment was spread foolishly. Items and news about the beginning, cause, side effects, and fix and fear inspired notions about the infection were on the channel. This gets the need to assist the majority with having the option to not succumb to this phony or one-sided news utilizing ML calculations. The utilization of group AI calculations and vectorizers and the fundamental topic 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-
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.
Refbacks
- There are currently no refbacks.