New Approaches to Extract Information From Posts on COVID-19 Published on Reddit

Gianluca Bonifazi (), Enrico Corradini (), Domenico Ursino and Luca Virgili ()
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Gianluca Bonifazi: Department of Information Engineering, Polytechnic University of Marche, 60131, Ancona, Via Brecce Bianche 12, Italy
Enrico Corradini: Department of Information Engineering, Polytechnic University of Marche, 60131, Ancona, Via Brecce Bianche 12, Italy
Domenico Ursino: Department of Information Engineering, Polytechnic University of Marche, 60131, Ancona, Via Brecce Bianche 12, Italy
Luca Virgili: Department of Information Engineering, Polytechnic University of Marche, 60131, Ancona, Via Brecce Bianche 12, Italy

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 05, 1385-1431

Abstract: In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.

Keywords: COVID-19; Reddit; information extraction; hierarchical classification; backtracking; social network analysis; community detection (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219622022500213

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