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Fake News Detection System

Authors: Supriya S. Telsang; Pranav M. Pendse; Pranav R. Dhanayate; Pranav V. Apsingekar; Sachin A. Prasad; Pratha P. Sawant; Pratyunsh Katkar;

Fake News Detection System

Abstract

Abstract: In this research paper, we are creating a model to help identify fake news using a type of computer learning called logistic regression. Fake News is the news or data that can mislead to the whole countries people . So our system will identify if the news or the article is real or fake by checking . Using logistic regression , we aim to give the output of the news whether its fake or real very quickly and accurately .For training the model , we will use the set tagged news articles to help the model to identify that if the news is fake or real patterns . Then we will test that how much our model is identifying the real and fake news correctly. This research aims to detect fake news by using computer algorithms to spot false stories.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold