We started looking into this topic towards the end of March 2020, when everyone was desperate about COVID-19, and when we started seeing reports of hate speech, physical attacks and harassment against people of Asian origin. After spending a lot of time in the realm of online social media, we started looking at this problem of hate speech, but on online platforms. Also, as hate speech was spreading on social media, there were also people opposing hate speech, in support of people of Asian descent: we had these two competing narratives spreading simultaneously on social media platforms. We then started collecting data on Twitter related to this phenomenon, starting in January 2020: we basically crawled millions of tweets from hundreds of thousands of users on these topics, and we did one of the first analyzes anti-Asian hate speech and counter-speech on social media.
First, we created a hand-labeled dataset with around 3,200 tweets to train a classifier. Next, we used our classifier to identify hate speech and counter-speech from the rest of the data. In total, we identified 1.3 million tweets containing anti-Asian hate speech and 1.1 million tweets containing counter-speech. With this large-scale data, we began to do different types of analysis to understand how hateful comments spread, how users spread both hate speech and counter-speech, and how these two narratives are shared. influence each other. One of the most important findings we had was that the more hate speech you see, the more likely you are to make hateful comments: if many of your friends, that is, if many people in your social platform neighborhood, spread hate, you are also more likely to spread hate. In other words, hate speech is contagious! However, there is some hope, as we found initial evidence that counter-speech can slightly prevent hate speech from being picked up by others: there is a small inhibition effect in terms of counter-speech that can prevent users from making hateful comments in the first place.
Again, we see the same theme here that regular users are one of the most effective ways to combat malicious actors and activity by speaking out. Essentially, we not only need IT tools to help us identify these malicious activities, but we also need community efforts to effectively counter these issues. We need regular users to be more aware of these issues, and we need them to be more proactive and speak up – for example, by simply reporting inappropriate content – when they see bad behavior.