English

Understanding Stay-at-home Attitudes through Framing Analysis of Tweets

Social and Information Networks 2022-09-14 v1

Abstract

With the onset of the COVID-19 pandemic, a number of public policy measures have been developed to curb the spread of the virus. However, little is known about the attitudes towards stay-at-home orders expressed on social media despite the fact that social media are central platforms for expressing and debating personal attitudes. To address this gap, we analyze the prevalence and framing of attitudes towards stay-at-home policies, as expressed on Twitter in the early months of the pandemic. We focus on three aspects of tweets: whether they contain an attitude towards stay-at-home measures, whether the attitude was for or against, and the moral justification for the attitude, if any. We collect and annotate a dataset of stay-at-home tweets and create classifiers that enable large-scale analysis of the relationship between moral frames and stay-at-home attitudes and their temporal evolution. Our findings suggest that frames of care are correlated with a supportive stance, whereas freedom and oppression signify an attitude against stay-at-home directives. There was widespread support for stay-at-home orders in the early weeks of lockdowns, followed by increased resistance toward the end of May and the beginning of June 2020. The resistance was associated with moral judgment that mapped to political divisions.

Keywords

Cite

@article{arxiv.2209.05729,
  title  = {Understanding Stay-at-home Attitudes through Framing Analysis of Tweets},
  author = {Zahra Fatemi and Abari Bhattacharya and Andrew Wentzel and Vipul Dhariwal and Lauren Levine and Andrew Rojecki and G. Elisabeta Marai and Barbara Di Eugenio and Elena Zheleva},
  journal= {arXiv preprint arXiv:2209.05729},
  year   = {2022}
}

Comments

This paper has been accepted at The IEEE International Conference on Data Science and Advanced Analytics (DSAA)