  The online hate speech is proliferating with several organization and
countries implementing laws to ban such harmful speech. While these
restrictions might reduce the amount of such hateful content, it does so by
restricting freedom of speech. Thus, an promising alternative supported by
several organizations is to counter such hate speech with more speech. In this
paper, We analyze hate speech and the corresponding counters (aka
counterspeech) on Twitter. We perform several lexical, linguistic and
psycholinguistic analysis on these user accounts and obverse that counter
speakers employ several strategies depending on the target community. The
hateful accounts express more negative sentiments and are more profane. We also
find that the hate tweets by verified accounts have much more virality as
compared to a tweet by a non-verified account. 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. We also build
a supervised model for classifying the hateful and counterspeech accounts on
Twitter and obtain an F-score of 0.77. We also make our dataset public to help
advance the research on hate speech.
