  In this research, we investigate techniques to detect hate speech in movies.
We introduce a new dataset collected from the subtitles of six movies, where
each utterance is annotated either as hate, offensive or normal. We apply
transfer learning techniques of domain adaptation and fine-tuning on existing
social media datasets, namely from Twitter and Fox News. We evaluate different
representations, i.e., Bag of Words (BoW), Bi-directional Long short-term
memory (Bi-LSTM), and Bidirectional Encoder Representations from Transformers
(BERT) on 11k movie subtitles. The BERT model obtained the best macro-averaged
F1-score of 77%. Hence, we show that transfer learning from the social media
domain is efficacious in classifying hate and offensive speech in movies
through subtitles.
