Abstract
The hashtag #JeNeSuisPasUnVirus (#IamNotAVirus) was coined in January 2020 during the outbreak of coronavirus (COVID-19) in China as anti-Asian racist incidents gained visibility nourished by the idea that if the pandemic originated in Asia, Asian people were infected and responsible for the spread of the virus. This hashtag reached a peak on January 28th before decreasing, following the shifting curve of the blame phenomenon (Atlani-Duault et al., 2020). Certainly anti-Asian racism is not a new phenomenon, but the Covid-19 pandemic came as an enhancer for xenophobic acts and hate speeches. As Asian communities informally got together online via hashtag activism to denounce persecutions they face, we could observe how the recurring blame process amid health crises, has been worded around ethnic and cultural stigmata. The many comparisons Twitter users from our corpus tended to make with anti-Muslim sentiments in France showed just how the phenomenon at stake here is the one of using a nation's minorities as a scapegoat for local issues. This 2020 epidemic and its associated Twitter hashtag #JeNeSuisPasUnVirus, are just a salience that ought to be grasped by researchers to scrutinize the plurality of narratives around anti-Asian racism and observe how the blame phenomenon works. The present study aims to do so by applying text mining methods to thousands of tweets containing this precise hashtag from the end of January to the end of March 2020.
The present article stands for a Master Thesis presented in order to obtain a M.A. in Data Sciences and Digital Sociology from Gustave Eiffel University under the supervision of Digital Sociology associate professor Bilel Benbouzid. It hasn't been published.
See all documents refering Cortext Manager
