Online Hate: Behavioural Dynamics and Relationship with Misinformation
This addresses the problem of online hate speech and polarization for social media platforms and policymakers, though it is incremental in applying existing detection methods to new data.
The study analyzed over one million YouTube comments to detect hate speech and found no evidence of 'serial haters,' but users aligned with questionable or reliable video channels used more toxic language in opposing communities, with overall toxicity increasing with discussion length.
Online debates are often characterised by extreme polarisation and heated discussions among users. The presence of hate speech online is becoming increasingly problematic, making necessary the development of appropriate countermeasures. In this work, we perform hate speech detection on a corpus of more than one million comments on YouTube videos through a machine learning model fine-tuned on a large set of hand-annotated data. Our analysis shows that there is no evidence of the presence of "serial haters", intended as active users posting exclusively hateful comments. Moreover, coherently with the echo chamber hypothesis, we find that users skewed towards one of the two categories of video channels (questionable, reliable) are more prone to use inappropriate, violent, or hateful language within their opponents community. Interestingly, users loyal to reliable sources use on average a more toxic language than their counterpart. Finally, we find that the overall toxicity of the discussion increases with its length, measured both in terms of number of comments and time. Our results show that, coherently with Godwin's law, online debates tend to degenerate towards increasingly toxic exchanges of views.