Methods of Informational Trends Analytics and Fake News Detection on Twitter
Bohdan M. Pavlyshenko
TL;DR
This paper explores various analytical methods, including deep learning, graph theory, and association rules, to study news trends and detect fake news on Twitter, with a case study on the Russian invasion of Ukraine in 2022.
Contribution
It introduces a multi-method approach combining deep learning, graph theory, and association rules for analyzing news trends and fake news detection on Twitter.
Findings
Deep learning effectively detects fake news.
Graph theory reveals news trend patterns.
Association rules identify key informational trends.
Abstract
In the paper, different approaches for the analysis of news trends on Twitter has been considered. For the analysis and case study, informational trends on Twitter caused by Russian invasion of Ukraine in 2022 year have been studied. A deep learning approach for fake news detection has been analyzed. The use of the theory of frequent itemsets and association rules, graph theory for news trends analytics have been considered.
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Taxonomy
TopicsInformation Systems and Technology Applications · Scientific Research and Philosophical Inquiry