  With the ongoing debate on 'freedom of speech' vs. 'hate speech' there is an
urgent need to carefully understand the consequences of the inevitable
culmination of the two, i.e., 'freedom of hate speech' over time. An ideal
scenario to understand this would be to observe the effects of hate speech in
an (almost) unrestricted environment. Hence, we perform the first temporal
analysis of hate speech on Gab.com, a social media site with very loose
moderation policy. We first generate temporal snapshots of Gab from millions of
posts and users. Using these temporal snapshots, we compute an activity vector
based on DeGroot model to identify hateful users. The amount of hate speech in
Gab is steadily increasing and the new users are becoming hateful at an
increased and faster rate. Further, our analysis analysis reveals that the hate
users are occupying the prominent positions in the Gab network. Also, the
language used by the community as a whole seem to correlate more with that of
the hateful users as compared to the non-hateful ones. We discuss how, many
crucial design questions in CSCW open up from our work.
