Hate begets Hate: A Temporal Study of Hate Speech

TL;DR

This study conducts a temporal analysis of hate speech on Gab.com, revealing increasing hate speech, faster hateful user onboarding, and the prominence of hateful users in the network, highlighting implications for online community design.

Contribution

First temporal analysis of hate speech on Gab.com, showing how hate speech evolves and spreads in an almost unrestricted social media environment.

Findings

Hate speech steadily increases over time.

Hateful users occupy central network positions.

Community language increasingly aligns with hateful users.

Abstract

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.