A statistical approach for reducing misinformation propagation on twitter social media

Saxena, N and Sinha, A and Bansal, T and Wadhwa, A (2023) A statistical approach for reducing misinformation propagation on twitter social media. In: Information Processing and Management, 60 (4).

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Abstract

Misinformation on Twitter has caused havoc all over the world, thus posing a prime cybersecurity concern. Hence, our research objective is to tackle the menace of infodemic by computing the credibility of Twitter users. Our proposed approach rates the Twitter users on a scale of credibility rating, obtained by analysing the pattern of growth rate statistics of the tweeting and following behaviour. Hence, our research provides two types of credibility computational frameworks that includes Twitter-user profile-centric activities and tweet-propagation features. Initially, we establish and justify the fact that there is a observable difference in the propagation pattern of rumored and non-rumored tweets by analysing the posts of 129,323 Twitter users. Later, the credibility score is devised based on tweet propagation pattern. It contains parameters that satisfies a statistical significance level of 99.99. The second credibility score formula utilizes more profile centric features and almost all the parameters used for second formula satisfy a statistical significance level of 95. Our research will significantly assist social media administrators to identify the rumor spreaders, thereby reducing the menace of misinformation. © 2023 Elsevier Ltd

Item Type:	Journal Article
Publication:	Information Processing and Management
Publisher:	Elsevier Ltd
Additional Information:	The copyright for this article belongs to Elsevier Ltd.
Keywords:	Growth rate; Statistics; User profile, Cyber security; Growth rate statistic; Mis-information propagation; Propagation graph; Propagation pattern; Social media; Statistical significance; Tweet propagation graph; Twitter social networks; User credibility, Social networking (online)
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	21 Apr 2023 09:56
Last Modified:	21 Apr 2023 09:56
URI:	https://eprints.iisc.ac.in/id/eprint/81354

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