With the online proliferation of hate URLanizations and governments are trying to tackle this issue, without upsetting the `freedom of speech '. In this paper, we try to understand the temporal effects of allowing hate speech on a platform (Gab) as a norm (protected as freedom of speech).We observe that the amount of hate speech  is steadily increasing , with new users being exposed to hate speech at a faster rate. We also observe that the language used by the Gab users are aligning more with the hateful users with time. We believe that the hate community is evolving a new language culture of their own in this unmoderated environment and the rest of the (benign) population is slowly getting adapted to this new language culture. Our workprovides empirical observations to the HCI questions regarding the freedom of hate speech .
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 .
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online proliferation of
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
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URLanizations and governments are trying to tackle this issue, without upsetting the `freedom of
R
'. In this paper, we try to understand the temporal effects of allowing hate speech on a platform (Gab) as a norm (protected as freedom of speech).We observe that the
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
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in Gab
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, with new users being exposed to hate speech at a
and the new users are becoming hateful at an increased and
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We also observe that the
Further, our analysis analysis reveals that the hate users are occupying the prominent positions in the Gab network. Also, the
R
Gab users are aligning more with
community as a whole seem to correlate more with that of
R
with time. We believe that the hate community is evolving a new language culture of their own in this unmoderated environment and the rest of the (benign) population is slowly getting adapted to this new language culture. Our workprovides empirical observations to the HCI questions regarding the freedom of hate speech
as compared to the non-hateful ones. We discuss how, many crucial design questions in CSCW open up from our work