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
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.