Linking COVID-19 perception with socioeconomic conditions using Twitter data

Date

2021-07-01

Author

Ertekin Bolelli, Şeyda
Okan, Oral
Ozbilen, Alper
Ozdemir, Suat

Metadata

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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We, as humans, are constantly in relation with our environment. Sudden changes in our living media may alter the way we perceive ourselves and our environment in various ways. Coronavirus (COVID-19) outbreak is a great example of a sudden change. The outbreak influenced means of work, study, socialization, and communication in unprecedented ways. In our study, we investigate the topic dynamics of Twitter content sharing for the Republic of Turkey. We have analyzed 1.3 million tweets containing the keyword ``korona'' shared between February 24, 2020 and May 2, 2020. Our study has three key outcomes. The first one is, after the official announcement of first COVID-19 case in Turkey, rate of COVID-19-related content sharing decreases and hygiene-, lifestyle-, and anxiety-related tweets increase. Second, we see that a number of friends and followers influence content sharing dynamics where accounts sharing COVID-19 News-related content have more followers compared to accounts that share the remaining contents. Finally, motivated by the World Bank's Poverty Monitoring Technical Note, we inquired the effect of income on content sharing and found that GDP per capita of the author's city is more influential on COVID-19 News-related content sharing compared to the population and COVID-19 cases per 1,00,000 people. The lower the GDP per capita, the higher the COVID-19 News-related content sharing is. Also, our model indicates that lower income and population along with high rates of COVID-19 cases per 1,00,000 people are associated with increased COVID-19 News-related content sharing.

URI

https://ieeexplore.ieee.org/document/9477210
https://hdl.handle.net/11511/91415

Journal

IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS

DOI

https://doi.org/10.1109/tcss.2021.3089657

Collections

Department of Computer Engineering, Article

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Citation Formats

Ş. Ertekin Bolelli, O. Okan, A. Ozbilen, and S. Ozdemir, “Linking COVID-19 perception with socioeconomic conditions using Twitter data,” IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, pp. 1–12, 2021, Accessed: 00, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9477210.