  Hate content in social media is ever-increasing. While Facebook, Twitter,
Google have attempted to take several steps to tackle the hateful content, they
have mostly been unsuccessful. Counterspeech is seen as an effective way of
tackling the online hate without any harm to the freedom of speech. Thus, an
alternative strategy for these platforms could be to promote counterspeech as a
defense against hate content. However, in order to have a successful promotion
of such counterspeech, one has to have a deep understanding of its dynamics in
the online world. Lack of carefully curated data largely inhibits such
understanding. In this paper, we create and release the first ever dataset for
counterspeech using comments from YouTube. The data contains 13,924 manually
annotated comments where the labels indicate whether a comment is a
counterspeech or not. This data allows us to perform a rigorous measurement
study characterizing the linguistic structure of counterspeech for the first
time. This analysis results in various interesting insights such as: the
counterspeech comments receive much more likes as compared to the
non-counterspeech comments, for certain communities majority of the
non-counterspeech comments tend to be hate speech, the different types of
counterspeech are not all equally effective and the language choice of users
posting counterspeech is largely different from those posting non-counterspeech
as revealed by a detailed psycholinguistic analysis. Finally, we build a set of
machine learning models that are able to automatically detect counterspeech in
YouTube videos with an F1-score of 0.71. We also build multilabel models that
can detect different types of counterspeech in a comment with an F1-score of
0.60.
