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arXiv · 2108.10724

How Hateful are Movies? A Study and Prediction on Movie Subtitles

Niklas von Boguszewski·Sana Moin·Anirban Bhowmick·Seid Muhie Yimam·Chris Biemann
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Abstract

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.

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Niklas von Boguszewski, Sana Moin, Anirban Bhowmick, Seid Muhie Yimam, Chris Biemann. 2021-08-19. How Hateful are Movies? A Study and Prediction on Movie Subtitles. https://arxiv.org/abs/2108.10724

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