The rise in hate speech targeting minority communities underscores the urgent need for effective tools to detect and address harmful content in digital communication. We examine over 3 million tweets posted by state legislators between 2020 and 2021, focusing on messages directed at Asian communities. To address the nuanced nature of hate speech, we develop three comprehensive definitions for identifying hate speech. With a human-in-the-loop approach, our fine-tuned BERT-NLI model achieved improved classification performance.
Recent work in digital politics has begun to explore the role of race and ethnicity in digital communications. This research, however, has not fully addressed how lawmakers interact with their Asian constituents and the broader minority population. We take up this task by analyzing over 3 million tweets posted by state legislators between 2020 and 2021, focusing on messages targeting Asian ethnic groups. We fine-tuned a large language on classification task by using our labelled data and detected 7,202 anti-racism speech and 2,536 racism speech among 25,102 tweets that target Asian ethnic groups specifically.