A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data

Jia Luo (1) , Daiyun Peng (1) , Lei Shi (2, 3) , Didier El Baz (4) , Xinran Liu (1)

1 Beijing University of Technology
2 Communication University of China
3 Guilin University of Electronic Technology
4 LAAS-SARA - Équipe Services et Architectures pour Réseaux Avancés

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 thirty-five 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; Word Frequency Analysis; Topic Clustering Analysis; Sentiment analysis

Domains: Data Analysis, Statistics and Probability [physics.data-an]

Jia Luo, Daiyun Peng, Lei Shi, Didier El Baz, Xinran Liu. A Comparative Analysis of the COVID-19 Infodemic in English and Chinese: Insights from Social Media Textual Data. Frontiers in Public Health, 2023, 11, ⟨10.3389/fpubh.2023.1281259⟩. ⟨hal-04283683⟩