A comparative analysis of the COVID-19 Infodemic in English and Chinese: insights from social media textual data

Jia Luo; Daiyun Peng; Lei Shi; Didier El Baz; Xinran Liu

doi:10.3389/fpubh.2023.1281259

A comparative analysis of the COVID-19 Infodemic in English and Chinese: insights from social media textual data

Front Public Health. 2023 Nov 10:11:1281259. doi: 10.3389/fpubh.2023.1281259. eCollection 2023.

Authors

Jia Luo^{1

2}, Daiyun Peng¹, Lei Shi^{3

4}, Didier El Baz⁵, Xinran Liu¹

Affiliations

¹ College of Economics and Management, Beijing University of Technology, Beijing, China.
² Chongqing Research Institute, Beijing University of Technology, Chongqing, China.
³ State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China.
⁴ Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China.
⁵ LAAS-CNRS, Université de Toulouse, Toulouse, France.

Abstract

The COVID-19 infodemic, characterized by the rapid spread of misinformation and unverified claims related to the pandemic, presents a significant challenge. This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms. To ensure a balanced representation, two infodemic datasets were created by augmenting previously collected social media textual data. Through word frequency analysis, the 30 most frequently occurring infodemic words are identified, shedding light on prevalent discussions surrounding the infodemic. Moreover, topic clustering analysis uncovers thematic structures and provides a deeper understanding of primary topics within each language context. Additionally, sentiment analysis enables comprehension of the emotional tone associated with COVID-19 information on social media platforms in English and Chinese. This research contributes to a better understanding of the COVID-19 infodemic phenomenon and can guide the development of strategies to combat misinformation during public health crises across different languages.

Keywords: COVID-19; infodemic data; sentiment analysis; topic clustering analysis; word frequency analysis.

MeSH terms

COVID-19* / epidemiology
COVID-19* / psychology
Humans
Infodemic*
Language*
Social Media*

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work is supported by the Natural Science Foundation of Chongqing, China (Grant No. CSTB2023NSCQ-MSX0391), the National Natural Science Foundation of China (Grant No. 72104016), the R&D Program of the Beijing Municipal Education Commission (Grant No. SM202110005011), and the Guangxi Key Laboratory of Trusted Software (Grant No. KX202315).