Abstract
With the ongoing debate on ‘freedom of speech’ vs. ‘hate speech,’ there is an urgent need to carefullyunderstand 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 generatetemporal snapshotsof Gab from millions of posts and users. Using these temporal snapshots, we compute anactivity vectorbased on DeGroot model to identify hateful users. The amount of hate speech in Gab is steadily increasing and the new users are becoming hatefulat 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 tocorrelate 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.