A deep learning approach for detecting the behaviour of people having personality disorders towards COVID-19 from Twitter. (28th June 2022)

• Record Type: Journal Article
• Authors: Ellouze, Mourad; Mechti, Seifeddine; Krichen, Moez; Ravi, Vinayakumar; Belguith, Lamia Hadrich
• Is Part Of: International journal of computational science and engineering. Volume 25:Number 4(2022)
• Page Start: 353
• Page End: 366
• Publication Date: 2022-06-28
• Subjects: COVID-19 -- personality disorder -- text mining -- natural language processing -- deep learning -- Twitter; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: 1) detect paranoid people by classifying their set of tweets into two classes (paranoid/not-paranoid); 2) ensure the surveillance of these people by classifying their tweets about COVID-19 into two classes (person with normal behaviour, person with inappropriate behaviour). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behaviour of these people towards COVID-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.