Literature DB >> 35582463

Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19.

Hanjia Lyu1, Long Chen2, Yu Wang3, Jiebo Luo2.   

Abstract

With the world-wide development of 2019 novel coronavirus, although WHO has officially announced the disease as COVID-19, one controversial term - "Chinese Virus" is still being used by a great number of people. In the meantime, global online media coverage about COVID-19-related racial attacks increases steadily, most of which are anti-Chinese or anti-Asian. As this pandemic becomes increasingly severe, more people start to talk about it on social media platforms such as Twitter. When they refer to COVID-19, there are mainly two ways: using controversial terms like "Chinese Virus" or "Wuhan Virus", or using non-controversial terms like "Coronavirus". In this article, we attempt to characterize the Twitter users who use controversial terms and those who use non-controversial terms. We use the Tweepy API to retrieve 17 million related tweets and the information of their authors. We find the significant differences between these two groups of Twitter users across their demographics, user-level features like the number of followers, political following status, as well as their geo-locations. Moreover, we apply classification models to predict Twitter users who are more likely to use controversial terms. To our best knowledge, this is the first large-scale social media-based study to characterize users with respect to their usage of controversial terms during a major crisis.

Entities:  

Keywords:  COVID-19; Classification; Twitter; controversial term; social media; user characterization

Year:  2020        PMID: 35582463      PMCID: PMC8851431          DOI: 10.1109/TBDATA.2020.2996401

Source DB:  PubMed          Journal:  IEEE Trans Big Data        ISSN: 2332-7790


  5 in total

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3.  Who tweets? Deriving the demographic characteristics of age, occupation and social class from twitter user meta-data.

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4.  Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science.

Authors:  Adrián A Díaz-Faes; Timothy D Bowman; Rodrigo Costas
Journal:  PLoS One       Date:  2019-05-22       Impact factor: 3.240

5.  Characterizing the followers and tweets of a marijuana-focused Twitter handle.

Authors:  Patricia Cavazos-Rehg; Melissa Krauss; Richard Grucza; Laura Bierut
Journal:  J Med Internet Res       Date:  2014-06-27       Impact factor: 5.428

  5 in total
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1.  How racial animus forms and spreads: Evidence from the coronavirus pandemic.

Authors:  Runjing Lu; Sophie Yanying Sheng
Journal:  J Econ Behav Organ       Date:  2022-05-27
  1 in total

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