"Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19 (2004.04046v2)

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.

• The paper analyzes the emergence and evolution of Sinophobic rhetoric on Twitter and 4chan's /pol/ during the COVID-19 pandemic using temporal, contextual, and semantic analysis.
• Temporal analysis revealed surges in Sinophobic language on both platforms coinciding with global events, but with distinct slurs ('chinazi' on Twitter, 'chink' on /pol/) reflecting different community norms.
• The study demonstrates how real-world crises exacerbate online xenophobia, leading to the entrenchment of derogatory terms and highlighting the need for interventions to curb online hate speech.

The Emergence and Dynamics of Sinophobic Behavior on Online Platforms During the COVID-19 Pandemic

The paper "Go eat a bat, Chang!: On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19" provides a meticulous examination of the proliferation and specific evolution of Sinophobic rhetoric across two distinct social media platforms, Twitter and 4chan's /pol/, during the ongoing COVID-19 pandemic. Through a robust multi-method analysis involving temporal, contextual, and semantic methodologies, the researchers present a comprehensive assessment of how discussions surrounding Sinophobia have evolved with the global escalation of the pandemic.

The paper's primary objective is to delineate the trajectories of racist discourse targeted at Chinese and Asian communities in response to SARS-CoV-2's origin from Wuhan, China. The researchers systematically collected large datasets from both Twitter—a mainstream platform—and 4chan’s /pol/—a fringe online community—spanning from late October 2019 to March 2020. The temporal analysis is the cornerstone of the paper, revealing spikes in discussions and the use of Sinophobic language coinciding with notable global events, such as China's lockdown of Wuhan and President Trump's tweet labeling COVID-19 as the "Chinese Virus."

Key findings from the temporal analysis elucidate the rise of Sinophobic content as a cross-platform phenomenon. While both platforms demonstrated increased occurrences of specific Sinophobic slurs, distinct differences were evident. Twitter saw frequent use of "chinazi," a denigrating mashup conflating "China" and "Nazi," often absent from /pol/, where "chink" was more prevalent. This suggests that while international political events galvanize online discussions, the rhetorical approach varies based on community norms and user base characteristics.

The research notably employs word embedding techniques to investigate the semantic trajectory and contextual framing of discourse involving terms like "china," "chinese," and "virus." This analysis highlights the introduction and entrenchment of derogatory terms such as "kungflu" in /pol/ and "chinaisasshoe" on Twitter. Such emergent terms underscore the dynamic nature of online language, particularly when festering across ideologically opposed platforms as the pandemic unfolds.

Through visual and quantitative analysis, the paper identifies that Sinophobic language has transitioned into well-knit semantic communities on /pol/, underlined by derogatory terms juxtaposed with conspiracies suggesting the virus as a "bioattack." Contrastingly, the Twitter analysis displays a varied synthesis of political commentary and Sinophobia, suggesting both platforms, while sharing toxic discourse, harbor unique lexicons and interaction patterns.

The implications of this paper are salient for both academia and society at large, accentuating how real-world crises like COVID-19 can exacerbate already-existing xenophobic sentiments online, which may perpetuate real-world violence and discrimination. The paper effectively demonstrates the susceptibility of digital platforms to serve as conduits for such rhetoric and calls for a conscientious, collaborative effort in mitigating the resultant societal harm.

Theoretically, the research invites further inquiry into cross-platform dynamics of hate speech evolution and its socio-political impact. Future research could expand upon these findings by exploring the role of platform moderation policies, user anonymity, and dissemination dynamics amidst the shifting socio-political landscapes.

Collectively, this paper contributes significantly to the extant literature on online racism and misinformation by highlighting the evolution and semantic drift of hate during a global crisis and provides critical insights for developing strategic interventions to curb online hate speech proliferation.