Title: A deep learning approach for detecting the behaviour of people having personality disorders towards COVID-19 from Twitter

Authors: Mourad Ellouze; Seifeddine Mechti; Moez Krichen; Vinayakumar Ravi; Lamia Hadrich Belguith

Addresses: ANLP Group, MIRACL Laboratory, FSEGS, University of Sfax, Tunisia ' ANLP Group, MIRACL Laboratory, FSEGS, University of Sfax, Tunisia ' ReDCAD Laboratory, University of Sfax, Tunisia; Faculty of CSIT, Al-Baha University, Saudi Arabia ' Centre for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia ' ANLP Group, MIRACL Laboratory, FSEGS, University of Sfax, Tunisia

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.

Keywords: COVID-19; personality disorder; text mining; natural language processing; deep learning; Twitter.

DOI: 10.1504/IJCSE.2022.124553

International Journal of Computational Science and Engineering, 2022 Vol.25 No.4, pp.353 - 366

Received: 26 Mar 2021
Accepted: 13 Aug 2021
Published online: 28 Jul 2022 *

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