Topic Analysis of Social Media Posts during the COVID-19 Pandemic: Evidence from Tweets in Turkish

Ioan Batrancea (), Mehmet Ali Balcı, Larissa M. Batrancea, Ömer Akgüller, Horia Tulai, Mircea-Iosif Rus, Ema Speranta Masca and Ioan Dan Morar
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Ioan Batrancea: Department of Economics and Business Administration, Babes-Bolyai University
Mehmet Ali Balcı: Department of Mathematics, Muğla Sıtkı Koçman University
Larissa M. Batrancea: Department of Business, Babes-Bolyai University
Ömer Akgüller: Department of Mathematics, Muğla Sıtkı Koçman University
Horia Tulai: Department of Economics and Business Administration, Babes-Bolyai University
Mircea-Iosif Rus: National Institute of Research and Development URBAN INCERC
Ema Speranta Masca: Faculty of Economics and Law, “George Emil Palade” University of Medicine, Pharmacy, Science and Technology
Ioan Dan Morar: Faculty of Economics, University of Oradea

Journal of the Knowledge Economy, 2024, vol. 15, issue 3, No 86, 12391 pages

Abstract: Abstract The aim of this study is to analyze the shift in the social media discourse during the COVID-19 pandemic. The sample included Turkish users on Twitter, who shared opinions about the pandemic between March 9 and October 31, 2020. The collected tweets were first classified with the Long Short-Term Memory (LSTM) architecture, which used the global vector for word representation embedding method. In addition, due to the grammatical and semantic structure of the Turkish language, we employed the Zemberek library for the text pre-processing stage. We analyzed data according to two categories: user-to-public posts and user-to-user posts. User-to-user data were investigated with effective social network analysis techniques. Empirical results showed that Twitter users posted and disseminated information mainly related to economy, politics and world topics.

Keywords: Social network analysis; Deep learning; Topic analysis; Network centrality (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13132-023-01565-6

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