Exploring Perceptions of Social Movements in Social Media during COVID-19

The prevalence and impact of hate speech on social media platforms have been extensively documented in the literature. This research has explored the perceptions of social movements in social media during COVID-19, specifically within the context of the StopAsianHate movement in Twitter, which emerged in response to the increasing incidents of anti-Asian hate crimes and hate speech witnessed during this period.

This research contributes to the exploration of social movements by understanding the impact of significant events and influential voices on social media conversations which can inform the strategies and messaging of social movements in the future. This research also contributes to the field of hate speech detection by providing a novel approach and methodology for categorizing hate speech tweets and analyzing the levels of hate speech in social media, which can aid more effective and targeted responses to hate speech. This was achieved by utilizing a combination of natural language processing (NLP), machine learning, and transfer learning techniques.