Analysis and Detection of Multilingual Hate Speech Using Transformer
  Based Deep Learning
Arijit Das Somashree Nandy Rupam Saha Srijan Das Diganta Saha
Hate speech is harmful content that directly attacks or promotes hatred
against members of groups or individuals based on actual or perceived aspects
of identity, such as racism, religion, or sexual orientation. This can affect
social life on social media platforms as hateful content shared through social
media can harm both individuals and communities. As the prevalence of hate
speech increases online, the demand for automated detection as an NLP task is
increasing. In this work, the proposed method is using transformer-based model
to detect hate speech in social media, like twitter, Facebook, WhatsApp,
Instagram, etc. The proposed model is independent of languages and has been
tested on Italian, English, German, Bengali. The Gold standard datasets were
collected from renowned researcher Zeerak Talat, Sara Tonelli, Melanie Siegel,
and Rezaul Karim. The success rate of the proposed model for hate speech
detection is higher than the existing baseline and state-of-the-art models with
accuracy in Bengali dataset is 89%, in English: 91%, in German dataset 91% and
in Italian dataset it is 77%. The proposed algorithm shows substantial
improvement to the benchmark method.