This paper is published in Volume-6, Issue-2, 2020
Area
Computer Engineering
Author
Ruchika Mahajan
Co-authors
Sanskruti Godbole, Vedanti Manjule, Prasanna Lohe
Org/Univ
Cummins College of Engineering for Women, Nagpur, Maharashtra, India
Pub. Date
18 March, 2020
Paper ID
V6I2-1259
Publisher
Keywords
Twitter API, Random forest, Natural language processing algorithms, Naive Bayes, Bagging.

Citationsacebook

IEEE
Ruchika Mahajan, Sanskruti Godbole, Vedanti Manjule, Prasanna Lohe. Fake news analyzer, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Ruchika Mahajan, Sanskruti Godbole, Vedanti Manjule, Prasanna Lohe (2020). Fake news analyzer. International Journal of Advance Research, Ideas and Innovations in Technology, 6(2) www.IJARIIT.com.

MLA
Ruchika Mahajan, Sanskruti Godbole, Vedanti Manjule, Prasanna Lohe. "Fake news analyzer." International Journal of Advance Research, Ideas and Innovations in Technology 6.2 (2020). www.IJARIIT.com.

Abstract

Social media is a platform which acts as a chain for spreading of diversified news. Its cheap cost, fast access, and quick circulation and broadcasting of information help people to look out and consume news from social media. In today’s era, online social media plays a crucial role during real-world practical events, especially climacteric events or events that gain huge social attention from the population of the world. Apart from legitimate news information, malicious content, mean-spirited, vindictive and misleading information is also put online during these events, which can result in harm, chaos and monetary loss in the practical world. In our paper, we draw attention to the role of Twitter in analyzing a major ongoing event of India: NRC and the Citizenship Amendment Act in spreading fake content about these events. We elaborate on the gathering, elucidation, and validation process in detail and perform much exploratory analysis on the recognition of linguistic differences in fake and real or legitimate news content. Also, we aim to find out the source or origin of the fake news and the sources who help to spread the fake content. Secondly, we aim to perform a set of learning experiments to find out accurate fake news and their sources. Besides, we provide a relative analysis of the automatic and manual identification of fake news with the legitimate informants of the news content. This paper proposes a system that identifies unreliable and false news after analyzing and computing the set of data. This system aims to use various NLP algorithms and classification algorithms or techniques to help achieve maximum accuracy in finding the fake news.
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