  With the rise of voice chat rooms, a gigantic resource of data can be exposed
to the research community for natural language processing tasks. Moderators in
voice chat rooms actively monitor the discussions and remove the participants
with offensive language. However, it makes the hate speech detection even more
difficult since some participants try to find creative ways to articulate hate
speech. This makes the hate speech detection challenging in new social media
like Clubhouse. To the best of our knowledge all the hate speech datasets have
been collected from text resources like Twitter. In this paper, we take the
first step to collect a significant dataset from Clubhouse as the rising star
in social media industry. We analyze the collected instances from statistical
point of view using the Google Perspective Scores. Our experiments show that,
the Perspective Scores can outperform Bag of Words and Word2Vec as high level
text features.
