Country Image in COVID-19 Pandemic: A Case Study of China

TL;DR

This study uses aspect-based sentiment analysis on Twitter data to examine how China's image changed during COVID-19, revealing a shift from positive to negative sentiments and differing attitudes among various user groups.

Contribution

First to analyze country image during COVID-19 with fine-grained aspect-based sentiment analysis on large-scale social media data.

Findings

Overall sentiment shifted from non-negative to negative.

Negative ideology-related aspects increased in mentions.

Different Twitter user groups showed varied attitudes.

Abstract

Country image has a profound influence on international relations and economic development. In the worldwide outbreak of COVID-19, countries and their people display different reactions, resulting in diverse perceived images among foreign public. Therefore, in this study, we take China as a specific and typical case and investigate its image with aspect-based sentiment analysis on a large-scale Twitter dataset. To our knowledge, this is the first study to explore country image in such a fine-grained way. To perform the analysis, we first build a manually-labeled Twitter dataset with aspect-level sentiment annotations. Afterward, we conduct the aspect-based sentiment analysis with BERT to explore the image of China. We discover an overall sentiment change from non-negative to negative in the general public, and explain it with the increasing mentions of negative ideology-related aspects and decreasing mentions of non-negative fact-based aspects. Further investigations into different groups of Twitter users, including U.S. Congress members, English media, and social bots, reveal different patterns in their attitudes toward China. This study provides a deeper understanding of the changing image of China in COVID-19 pandemic. Our research also demonstrates how aspect-based sentiment analysis can be applied in social science researches to deliver valuable insights.