Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter | Zendy

Yi Zhao | Zendy; Haixu Xi | Zendy; Chengzhi Zhang | Zendy

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Exploring Occupation Differences in Reactions to COVID-19 Pandemic on Twitter

Author(s) -

Yi Zhao,

Haixu Xi,

Chengzhi Zhang

Publication year - 2020

Publication title -

data and information management

Language(s) - English

Resource type - Journals

ISSN - 2543-9251

DOI - 10.2478/dim-2020-0032

Subject(s) - latent dirichlet allocation , perspective (graphical) , covid-19 , social media , sentiment analysis , topic model , nothing , pandemic , valence (chemistry) , preference , psychology , computer science , natural language processing , world wide web , artificial intelligence , disease , medicine , statistics , mathematics , infectious disease (medical specialty) , pathology , virology , outbreak , philosophy , physics , epistemology , quantum mechanics

Coronavirus disease 2019 (COVID-19) pandemic-related information are flooded on social media, and analyzing this information from an occupational perspective can help us to understand the social implications of this unprecedented disruption. In this study, using a COVID-19-related dataset collected with the Twitter IDs, we conduct topic and sentiment analysis from the perspective of occupation, by leveraging Latent Dirichlet Allocation (LDA) topic modeling and Valence Aware Dictionary and sEntiment Reasoning (VADER) model, respectively. The experimental results indicate that there are significant topic preference differences between Twitter users with different occupations. However, occupation-linked affective differences are only partly demonstrated in our study; Twitter users with different income levels have nothing to do with sentiment expression on covid-19-related topics.

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