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Country Image in COVID-19 Pandemic: A Case Study of China
arXiv - CS - Social and Information Networks Pub Date : 2020-09-12 , DOI: arxiv-2009.05817 Huimin Chen, Zeyu Zhu, Fanchao Qi, Yining Ye, Zhiyuan Liu, Maosong Sun, Jianbin Jin
arXiv - CS - Social and Information Networks Pub Date : 2020-09-12 , DOI: arxiv-2009.05817 Huimin Chen, Zeyu Zhu, Fanchao Qi, Yining Ye, Zhiyuan Liu, Maosong Sun, Jianbin Jin
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.
更新日期：2020-09-17
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