Tracking onlinehatespeech that follows real-world events plos PLOSONE
It is therefore crucial to continue to analyze how mainstream platforms’ content moderation affects the overall ecology of online hate speech. In particular, our findings raise several research questions. First, given the prior findings of decreases of extremist content on YouTube and our finding of increases in hate speech on other moderated platforms, we need a better of understanding of the conditions under which platforms can be more effective in moderating content.
Finally, and perhaps most importantly, we find that online hate speech reacts to differing offline events in different and sometimes counter-intuitive ways. We might conceptualize at least three types of offline events in terms of the patterns of hate speech that follow them. First would be offline events that could plausibly be followed by significant increases in hate speech , but that draw little reaction from online hate communities.
Our study faces two important limitations. It is not possible to identify the entire population of online hate communities and sample from it. The online ecology of social media platforms is vast and constantly changing. Tracking and capturing data on these platforms is also limited by terms-of-service and privacy concerns. It is therefore not possible to capture the entire ecology of online speech nor to confidently claim that any sample of such speech is representative of the ecology.
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