This paper studies the interactions between members of a discriminated minority, members of the majority group, and political leaders. We construct a novel dataset of all tweets posted by "White American" and Chinese users located in the United States from January to August 2020. Using a variety of supervised and unsupervised text-analysis techniques, we show that anti-Chinese discrimination on Twitter significantly increased following (i) the COVID-19 outbreak, and (ii) Donald Trump's tweet referring to COVID-19 as the "Chinese virus." We then study the reaction of the Chinese minority and find that, after Trump's tweet, Chinese users were significantly more likely to (i) tweet assimilation-related content, and (ii) tweet criticism against the Chinese Communist Party. The rise in assimilation-related content is generally stronger for users who were more integrated before the shock.
Discrimination and assimilation: Evidence from anti-Chinese sentiments in the United States
Lanzara, Gianandrea;Squicciarini, Mara P.
2025
Abstract
This paper studies the interactions between members of a discriminated minority, members of the majority group, and political leaders. We construct a novel dataset of all tweets posted by "White American" and Chinese users located in the United States from January to August 2020. Using a variety of supervised and unsupervised text-analysis techniques, we show that anti-Chinese discrimination on Twitter significantly increased following (i) the COVID-19 outbreak, and (ii) Donald Trump's tweet referring to COVID-19 as the "Chinese virus." We then study the reaction of the Chinese minority and find that, after Trump's tweet, Chinese users were significantly more likely to (i) tweet assimilation-related content, and (ii) tweet criticism against the Chinese Communist Party. The rise in assimilation-related content is generally stronger for users who were more integrated before the shock.| File | Dimensione | Formato | |
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