Abstract
Everyone has internet access and is connected to social media in today's fast-paced world. Numerous pieces of data are disseminated on these websites, but there is no reliable source for confirmation or verification. This is where rumors come into play. Rumors are deliberate fabrications intended to sway or drastically alter popular opinion, and their impact can be seen in politics, especially during elections, and on social media. Thus, to resolve this problem, a rumor detector is needed that is capable of accurately indicating whether information is false or real. We implemented algorithms such as Multinomial Naive Bayes, Gradient Boosting, and Random Forest on complex datasets to get this Rumor Detection System closer to more reliable rumor performance. Accuracy of Multinomial Naive Bayes is approximately 90.4Forestitwas86.588.3
Highlights
In the 2000 rupee note, there will be a chip that can monitor where the note is with our exact coordinates, and it will be available from November 10, 2016. This rumor spread like wildfire on social media, with numerous news outlets supporting it
It had an effect on ordinary people and on people all over the world. Many such rumors about currencies, a few general awareness rumors about Kerala relief funds, and a slew of other false information began to circulate around the nation
We present rumor as an outlier by demonstrating the disproportionate amount of rumors compared to non-rumors
Summary
In the 2000 rupee note, there will be a chip that can monitor where the note is with our exact coordinates, and it will be available from November 10, 2016. This rumor spread like wildfire on social media, with numerous news outlets supporting it. It had an effect on ordinary people and on people all over the world. This paper presents a comprehensive view of the method of determining whether news is accurate or not, as well as the algorithms used and the results obtained. We analyzed it and used various classification algorithms such as Multinomial Naive Bayes, Random Forest, and gradient boosting to produce outputs
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