Abstract:
"Hate Speech has been an issue since the dawn of human communication. Hate speech can be
distinguished in multiple ways according to race, gender, religion, age and many ways that
divide people. Ever since the internet saw light, The users have the function to express their ideas
and views openly to the public without considering offensive language and hate speech. There
are multiple attempts and approaches done to this domain to limit and prevent public hate
speech. Social media plays the biggest role in this sector. In that YouTube is a video posting,
viewing platform with free speech limited by YouTube policies which sometimes become
ineffective. Therefore a system must be set in place with high accuracy using natural language
processing (NLP) algorithms.
Multiple approaches were taken to solve this arising issue by implementing single model
approaches, hybrid model approaches and ensemble model approaches. These systems were
done by using various algorithms such as Naive Bayes, Bidirectional Encoder Representation for
Transformers (BERT), Long Short Term Memory (LSTM) and many more. Mostly single model
implementations were conducted by many authors for detecting hate speech. Therefore presented
in this research paper, the author proposes a development of a hate speech detection system for
YouTube videos. The author provides a chrome extension and a single page web application
integrated with the ensemble modeling architecture. This gives the user a user friendly interface
and an easy to use architecture.
A kaggle labeled dataset of hate speech was used to train the BILSTM and BERT models. Both
these models produced stable and robust implementations. BILSTM performs by two
unidirectional LSTM layers. Both these layers return a probability vector. The BILSTM model
performs with an average of 98% accuracy. The BERT model performs with an average of 92%
accuracy."
