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

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 Mem...

Full description

Saved in:
Bibliographic Details
Published in:Journal of the knowledge economy 2023-11, Vol.15 (3), p.12361-12391
Main Authors: Batrancea, Ioan, Balcı, Mehmet Ali, Batrancea, Larissa M., Akgüller, Ömer, Tulai, Horia, Rus, Mircea-Iosif, Masca, Ema Speranta, Morar, Ioan Dan
Format: Article
Language:English
Subjects:
Access to information
Anxiety
Artificial intelligence
Communication
Competitive intelligence
Consumers
Corporate governance
COVID-19
Disease transmission
Economic Policy
Economics
Economics and Finance
Entrepreneurship
False information
Financial literacy
Gender pay gap
Knowledge economy
Pandemics
R & D/Technology Policy
R&D
Research & development
Social network analysis
Social networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1868-7873
1868-7865
1868-7873
DOI:10.1007/s13132-023-01565-6