21 Nov 2021

#DS4SocietySeminar 2021 <> Anti-Asian COVID-related Hate Speech and Stigma on Twitter: Dataset Creation to Algorithmic Detection

Syed Ishtiaque Ahmed

Talk Details


Since the advent of the COVID19 pandemic, individuals of Asian descent have been the subject of stigma and hate speech in both offline and online communities. One of the major venues for encountering such unfair attacks is social networks such as Twitter. In this study, we introduce a dataset of tweets containing sensitive opinions/news relevant to anti-Asian hate speech and stigma. This dataset can be used as a benchmark for further qualitative and quantitative research and analysis around this issue. To showcase the challenges of algorithmic detection of anti-Asian hate speech, we have also developed machine learning models which detect tweets containing COVID-related stigma and hate speech with the best accuracy of 76%, and therefore, can be used for automatic elimination of such behaviour in online communities. We believe this contribution significantly reduces the unfair stigma, hate speech, and discrimination against Asian people during the pandemic, as well as the post-COVID19 times.

Speaker Bio

Syed Ishtiaque Ahmed is an Assistant Professor of Computer Science at the University of Toronto, and the Director of the ‘Third Space’’ research group. His research focuses on the design challenges around strengthening the ‘voices’ or marginalized communities around the world. He conducted ethnography and built technologies with many underprivileged communities in Bangladesh, India, Pakistan, Iran, Iraq, Turkey, China, Canada, and the US. Ishtiaque received his PhD and Masters from Cornell University in the USA, and his Bachelor from BUET in Bangladesh. He received the International Fulbright Science and Technology Fellowship, Fulbright Centennial Fellowship, and Schwartz Reisman Institute Fellowship among others. His research has been funded by all three branches of Canadian tri-council research (NSERC, CIHR, SSHRC), NSF, NIH, Google, Microsoft, Facebook, Intel, Samsung, the World Bank, UNICEF, and UNDP, among others.

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