Methods of Informational Trends Analytics and Fake News Detection on Twitter
This addresses the issue of misinformation on social media for researchers and practitioners, but it appears incremental as it applies existing methods to a new dataset.
The paper tackled the problem of analyzing news trends and detecting fake news on Twitter, specifically studying trends related to the 2022 Russian invasion of Ukraine, and analyzed a deep learning approach for fake news detection along with methods like frequent itemsets and graph theory.
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.