With high prevalence of offensive language against the minorities in social media, counter hate speech generation is considered as an automatic way to tackle this challenge. The counter hate speeches are supposed to appear as a third voice to educate people and keep the social red lines bold without limiting the freedom of speech principles. The counter hate speechgeneration is based on the optimistic assumption that, any attempt to intervene the hate speeches in social media can play a positive role in this context. Beyond that, previous works ignored to investigate the sequence of comments before and after counter speech. To the best of our knowledge , no attempt has been made to measure the counter hate speech impact from statistical point of view . In this paper, we take the first step in this direction by measuring the counter hate speech impact on the next comments in terms of Google Perspective Scores. Furthermore, our experiments show that, counter hate speech can cause negative impacts, a phenomena which is called aggression in social media .
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 .
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high prevalence of offensive language against the minorities in social media, counter hate speech generation is considered as an automatic way to tackle this challenge. The counter hate speeches are supposed to appear as a third voice to educate people and keep the social red lines bold without limiting the freedom of speech principles. The counter hate speechgeneration is based on the optimistic assumption that, any attempt to intervene the hate speeches in social media can play a positive role in this context. Beyond that, previous works ignored to investigate the sequence of comments before and after counter speech.
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.
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, no attempt has been made to measure the counter hate speech impact from statistical point of view
all the hate speech datasets have been collected from text resources like Twitter
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in this direction by measuring the counter hate speech impact on the next comments in terms of
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
R
Furthermore, our
Our
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counter hate speech can cause negative impacts, a phenomena which is called aggression in social media
the Perspective Scores can outperform Bag of Words and Word2Vec as high level text features