Interaction dynamics between hate and counter users on Twitter

Abstract

Social media platforms usually tackle the proliferation of hate speech by blocking/suspending the message or account. One of the major drawback of such measures is the restriction of free speech. In this paper, we investigate the interaction of hatespeech and the responses that counter it (aka counter-speech). One of the prime contribution of this work is that we developed and released1 a dataset where we annotate pairs of hate and counter users. We perform several lexical, linguistic and psycholinguistic analysis on these annotated accounts and observe that the couterspeakers of the target communities employ different strategies to tackle the hatespeech. The hate users seem to be more popular as we observe that they are more subjective, express more negative sentiment, tweet more and have more followers. While the hate users seem to use words more about envy, hate, negative emotion, swearing terms, ugliness, the counter users use more words related to government, law, leader. Finally, we build a classifier to detect if a user is a hateful or counter speaker. This identification can help the platform to devise different incentive mechanisms to demote hate and promote counter speakers. Overall, our study unfolds for the first time, the interaction dynamics of the hate and counter users which could pave a more effective way for combating hate content on Twitter than just suspending the hate accounts.