  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.
