This Paper thinks of the utilizations of NLP (Natural Language Processing) methods for identifying the `phony news`, that is, deceiving news stories that originates from the non-respectable sources. Counterfeit news recognition is a basic yet testing issue in Natural Language Processing (NLP). The fast ascent of person to person communication stages has not just yielded an immense increment in data availability however has additionally quickened the spread of phony news. Given the gigantic measure of Web content, programmed counterfeit news recognition is a pragmatic NLP issue required by all online substance suppliers. This paper displays an overview on phony news discovery. Our overview presents the difficulties of programmed counterfeit news identification. We methodically survey the datasets and NLP arrangements that have been created for this task. We additionally talk about the breaking points of these datasets and issue plans, our bits of knowledge, and suggested arrangements. The fundamental target is to distinguish the phony news, which is a great content characterization issue with a straight forward recommendation. It is expected to manufacture a model that can separate between "Genuine" news and "Phony" news.

Natural Language Processing, Fake news detection, Data Mining, Machine Learning, Dataset

[1] M.Balmas, "Communication Research", SAGE, Vol.41, Issue.3, pp.430-454, 2014.
[2] A. Vlachos, S.Riedel, “Fact checking: Task definition and dataset construction”, In Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 18–22, 2014.
[3] V. Rubin, T. Vashchilko,” Identification of truth and deception in text: Application of vector space model to rhetorical structure theory”, In Proceedings of the Workshop on Computational Approaches to Deception Detection, pp. 97–106, 2012.
[4] H. Rashkin, E. Choi, J. Jang, S. Volkova, Y. Choi, “Truth of varying shades: Analyzing language in fake news and political fact-checking”, In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 2931–2937,2017.
[5] N. Ruchansky, S. Seo, Y. Liu,” A hybrid deep model for fake news detection”, In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 797–806, 2017.
[6] H. Karimi, P. Roy, S.Sadiya, J. Tang, ”Multi-source multi-class fake news detection”, In Proceedings of the 27th International Conference on Computational Linguistics, Location , pp. 1546–1557, 2018.
[7] A. Kirilin and M. Strube, “Exploiting a speaker’s credibility to detect fake news”, In Proceedings of Data Science, Journalism & Media workshop at KDD (DSJM18), 2018.
[8] S. Bhattacharjee, A. Talukder, B. Venkatram Balantrapu, "Active learning based news veracity detection with feature weighting and deep-shallow fusion", In the Proceedings of the 2017 IEEE International Conference on Big Data , Boston, USA, pp.556-565, 2017.
[9] N. Mehala, "Fake News Detection: A Survey" ,International Journal of Computer Sciences and Engineering , Vol.-7, Special Issue-16, pp.81-87, 2019.
[10] O. Ayankemi, “A Framework for Verifying the Authenticity of Banknote on the Automated Teller Machine (ATM) Using Possibilistic C-Means Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.57-63, 2018.
[11] V.Kapoor,”A New Cryptography Algorithm with an Integrated Scheme to Improve Data Security”, International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue-2, pp.39-46, 2013.