Since the start of the COVID-19 pandemic, there has been a widespread increase in the amount of hate-speech being propagated online against the Asian community.
This project builds upon and explores the work of He et al.
Their COVID-HATE dataset contains 206 million tweets focused around anti-Asian hate speech.
Using tweet data from before the COVID-19 pandemic, as well as the COVID-HATE dataset from He et al, we performed transfer learning.
We tested several different models, including BERT, RoBERTa, LSTM, and BERT-CNN.
Some of these models hindered the performance of He et al’s model, while others improved it.
You can access the original proposal here: Proposal
You can access the full report here: Report
We also explored the geographical extent of the ani-Asain tweets from the COVID-HATE dataset.
Since a large majority of the tweets originated from U.S.A., here is a map showing the distribution within this country.
