Predictive Embeddings for Hate Speech Detection on Twitter

By Rohan Kshirsagar, Tyus Cukuvac, K. McKeown, Susan McGregor. 2018

103 citations recorded by the source corpus.

We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.

Subfields: Artificial Intelligence, Communication, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Valerio Basile
2. Hao Sun
3. Zhexin Zhang
4. Gargi Ghosh
5. S. A. Chowdhury
6. Bernard Jim Jansen
7. Jelena Mitrović
8. Maximilian Hopf

In Communication

1. S. A. Chowdhury
2. Bernard Jim Jansen
3. Jelena Mitrović
4. Maximilian Hopf
5. Soon-gyo Jung
6. Tommaso Caselli
7. Hind Almerekhi
8. Michael Granitzer

In Natural Language Processing

1. Valerio Basile
2. Zhexin Zhang
3. Gargi Ghosh
4. S. A. Chowdhury
5. Bernard Jim Jansen
6. Hao Sun
7. Jelena Mitrović
8. Maximilian Hopf

Citations in the corpus

• Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (2023, background): “…networks like CNNs, RNNs, and LSTMs (Georgakopoulos et al., 2018; van Aken et al., 2018; Gunasekara and Nejadgholi, 2018; Joulin et al., 2017; Kshirsagar et al., 2018; Mishra et al., 2018; Mitrovi´c et al., 2019; Sigurbergsson and Derczynski, 2020), contemporary approaches increasingly favor…”
• Spread of Hate Speech in Online Social Media (2018, background): “The rise of hate speech: Hate speech has recently received a lot of research attention with several works that focus on detecting hate speech in online social media [4, 10, 12, 21].”
• Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, unknown): “…mentioned above, with different focuses such as hate speech detection Badjatiya, Gupta, Gupta, and Varma (2017); Fehn Unsvåg and Gambäck (2018); Kshirsagar, Cukuvac, McKeown, and McGregor (2018); Qian, ElSherief, Belding, and Wang (2018), author profiling in abuse detection Mishra, Tredici,…”
• HateBERT: Retraining BERT for Abusive Language Detection in English (2020, background): “…trend in NLP: feature-based linear classifiers (Waseem and Hovy, 2016; Ribeiro et al., 2018), neural network architectures (e.g., CNN or Bi-LSTM) (Kshirsagar et al., 2018; Mishra et al., 2018; Mitrovi´c et al., 2019), and, finally, fine-tuning pre-trained language models, e.g., BERT, RoBERTa,…”
• Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification (2019, methodology): “The automatic identification of hate speech has been mostly formulated as a natural language processing problem (e.g. Mishra et al., 2018; Gu-nasekara and Nejadgholi, 2018; Kshirsagar et al., 2018; Magu and Luo, 2018; Sahlgren et al., 2018).”
• Developing an online hate classifier for multiple social media platforms (2020, methodology): “[90] The GloVe model we apply is publicly available18 and contains 685k keys and 20k unique vectors with 300 dimensions, trained on Common Crawl datasets.”