  Despite the valuable social interactions that online media promote, these
systems provide space for speech that would be potentially detrimental to
different groups of people. The moderation of content imposed by many social
media has motivated the emergence of a new social system for free speech named
Gab, which lacks moderation of content. This article characterizes and compares
moderated textual data from Twitter with a set of unmoderated data from Gab. In
particular, we analyze distinguishing characteristics of moderated and
unmoderated content in terms of linguistic features, evaluate hate speech and
its different forms in both environments. Our work shows that unmoderated
content presents different psycholinguistic features, more negative sentiment
and higher toxicity. Our findings support that unmoderated environments may
have proportionally more online hate speech. We hope our analysis and findings
contribute to the debate about hate speech and benefit systems aiming at
deploying hate speech detection approaches.
