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Cited by 849

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Deep Learning for Hate Speech Detection in Tweets

Open access
Badjatiya, Pinkesh, Gupta, Shashank, Gupta, Manish, Varma, Vasudeva·2017·Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

Abstract

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.

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Metadata

Authors
Badjatiya, P., Gupta, S., Gupta, M., & Varma, V.
Venue
Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion
Publisher
ACM Press
Published
2017
Pages
759-760
OpenAlex
W2613977835
Cited by
849
References
1
FWCI
27.18
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Deep Learning for Hate Speech Detection in Tweets. https://app.cochat.ai/papers/10.1145/3041021.3054223
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Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017). Deep Learning for Hate Speech Detection in Tweets. Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion. https://doi.org/10.1145/3041021.3054223

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