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Free keywords:
cs.SI,Computer Science, Computers and Society, cs.CY
Abstract:
The outbreak of the COVID-19 pandemic has changed our lives in unprecedented
ways. In the face of the projected catastrophic consequences, many countries
have enacted social distancing measures in an attempt to limit the spread of
the virus. Under these conditions, the Web has become an indispensable medium
for information acquisition, communication, and entertainment. At the same
time, unfortunately, the Web is being exploited for the dissemination of
potentially harmful and disturbing content, such as the spread of conspiracy
theories and hateful speech towards specific ethnic groups, in particular
towards Chinese people since COVID-19 is believed to have originated from
China.
In this paper, we make a first attempt to study the emergence of Sinophobic
behavior on the Web during the outbreak of the COVID-19 pandemic. We collect
two large-scale datasets from Twitter and 4chan's Politically Incorrect board
(/pol/) over a time period of approximately five months and analyze them to
investigate whether there is a rise or important differences with regard to the
dissemination of Sinophobic content. We find that COVID-19 indeed drives the
rise of Sinophobia on the Web and that the dissemination of Sinophobic content
is a cross-platform phenomenon: it exists on fringe Web communities like
\dspol, and to a lesser extent on mainstream ones like Twitter. Also, using
word embeddings over time, we characterize the evolution and emergence of new
Sinophobic slurs on both Twitter and /pol/. Finally, we find interesting
differences in the context in which words related to Chinese people are used on
the Web before and after the COVID-19 outbreak: on Twitter we observe a shift
towards blaming China for the situation, while on /pol/ we find a shift towards
using more (and new) Sinophobic slurs.
