TY - GEN
T1 - Free speech or Free Hate Speech? Analyzing the Proliferation of Hate Speech in Parler
AU - Israeli, Abraham
AU - Tsur, Oren
N1 - Publisher Copyright:
© 2022 Association for Computational Linguistics.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - Social platforms such as Gab and Parler, branded as ‘free-speech’ networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit. In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating the limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make about 16% of Parler active users, and that they have distinct characteristics comparing to other user groups. We find that hate mongers are more active, more central, express distinct levels of sentiment, and convey a distinct array of emotions like anger and sadness. We further complement our analysis by comparing the trends observed in Parler to those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.
AB - Social platforms such as Gab and Parler, branded as ‘free-speech’ networks, have seen a significant growth of their user base in recent years. This popularity is mainly attributed to the stricter moderation enforced by mainstream platforms such as Twitter, Facebook, and Reddit. In this work we provide the first large scale analysis of hate-speech on Parler. We experiment with an array of algorithms for hate-speech detection, demonstrating the limitations of transfer learning in that domain, given the illusive and ever changing nature of the ways hate-speech is delivered. In order to improve classification accuracy we annotated 10K Parler posts, which we use to fine-tune a BERT classifier. Classification of individual posts is then leveraged for the classification of millions of users via label propagation over the social network. Classifying users by their propensity to disseminate hate, we find that hate mongers make about 16% of Parler active users, and that they have distinct characteristics comparing to other user groups. We find that hate mongers are more active, more central, express distinct levels of sentiment, and convey a distinct array of emotions like anger and sadness. We further complement our analysis by comparing the trends observed in Parler to those found in Gab. To the best of our knowledge, this is among the first works to analyze hate speech in Parler in a quantitative manner and on the user level.
UR - http://www.scopus.com/inward/record.url?scp=85139135480&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85139135480
T3 - WOAH 2022 - 6th Workshop on Online Abuse and Harms, Proceedings of the Workshop
SP - 109
EP - 121
BT - WOAH 2022 - 6th Workshop on Online Abuse and Harms, Proceedings of the Workshop
A2 - Narang, Kanika
A2 - Davani, Aida Mostafazadeh
A2 - Mathias, Lambert
A2 - Vidgen, Bertie
A2 - Talat, Zeerak
PB - Association for Computational Linguistics (ACL)
T2 - 6th Workshop on Online Abuse and Harms, WOAH 2022
Y2 - 14 July 2022
ER -