A comparative analysis of the Covid-19 Infodemic in English and Chinese

Insights from social media textual data

Dados Bibliográficos

ID22067976
AutoresJia Luo (0000-0002-0445-8794, Beijing University of Technology), Daiyun Peng (Beijing University of Technology), Lei Shi (0000-0003-1203-9984, Guilin University of Electronic Technology, autor correspondente), Didier El Baz (0000-0003-0427-0692, Centre National de la Recherche Scientifique), Xinran Liu (0000-0002-4455-1555, Beijing University of Technology)
Ano2023
Volume11
Páginas1281259-1281259
Data de publicação2023-11-10
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1281259
PMID38035290
OpenAlexW4388618872
IdiomaEN
Citações recebidas2
Referências citadas16

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

Data science · Misinformation · Natural language processing · Qualitative research · Sentiment analysis · Social media · Social media analytics · Social science · Sociology · Thematic analysis · World Wide Web · Computer Science · Data-Driven Disease Surveillance · History · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining