Xenophobic Bullying and COVID-19: An Exploration Using Big Data and Qualitative Analysis

Karla Dhungana Sainju, Huda Zaidi, Niti Mishra and Akosua Kuffour
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Karla Dhungana Sainju: Faculty of Social Science and Humanities, Ontario Tech University, 2000 Simcoe St. N., Oshawa, ON L1G 0C5, Canada
Huda Zaidi: Faculty of Social Science and Humanities, Ontario Tech University, 2000 Simcoe St. N., Oshawa, ON L1G 0C5, Canada
Niti Mishra: Rotman School of Management, University of Toronto, 105 St. George St., Toronto, ON M5S 3E, Canada
Akosua Kuffour: Faculty of Social Science and Humanities, Ontario Tech University, 2000 Simcoe St. N., Oshawa, ON L1G 0C5, Canada

IJERPH, 2022, vol. 19, issue 8, 1-19

Abstract: Extant literature suggests that xenophobic bullying is intensified by isolated national or global events; however, the analysis of such occurrences is methodologically limited to the use of self-reported data. Examining disclosures of racist bullying episodes enables us to contextualize various perspectives that are shared online and generate insights on how COVID-19 has exacerbated the issue. Moreover, understanding the rationale and characteristics present in xenophobic bullying may have important implications for our social wellbeing, mental health, and inclusiveness as a global community both in the short and long term. This study employs a mixed-method approach using Big Data techniques as well as qualitative analysis of xenophobic bullying disclosures on Twitter following the spread of COVID-19. The data suggests that about half of the sample represented xenophobic bullying. The qualitative analysis also found that 64% of xenophobic bullying-related tweets referred to occasions that perpetuated racist stereotypes. Relatedly, the rationale for almost 75% of xenophobic bullying incidents was due to being Chinese or Asian. The findings of this study, coupled with anti-hate reports from around the world, are used to suggest multipronged policy interventions and considerations of how social media sites such as Twitter can be used to curb the spread of misinformation and xenophobic bullying.

Keywords: xenophobic bullying; Twitter; COVID-19; social wellbeing; machine learning; qualitative analysis; misinformation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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