DeepHate: Hate Speech Detection via Multi-Faceted Text Representations

By Rui Cao, Roy Ka-Wei Lee, Tuan-Anh Hoang. 2020

110 citations recorded by the source corpus.

Online hate speech is an important issue that breaks the cohesiveness of online social communities and even raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many traditional machine learning and deep learning methods to detect hate speech in online social platforms automatically. However, most of these methods have only considered single type textual feature, e.g., term frequency, or using word embeddings. Such approaches neglect the other rich textual information that could be utilized to improve hate speech detection. In this paper, we propose DeepHate, a novel deep learning model that combines multi-faceted text representations such as word embeddings, sentiments, and topical information, to detect hate speech in online social platforms. We conduct extensive experiments and evaluate DeepHate on three large publicly available real-world datasets. Our experiment results show that DeepHate outperforms the state-of-the-art baselines on the hate speech detection task. We also perform case studies to provide insights into the salient features that best aid in detecting hate speech in online social platforms.

Subfields: Artificial Intelligence, Communication, Natural Language Processing, Safety, Robustness & Interpretability

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. Marco Guerini
2. Serra Sinem Tekiroğlu
3. Helena Bonaldi
4. Sara Dellantonio

In Communication

1. Marco Guerini
2. Serra Sinem Tekiroğlu
3. Helena Bonaldi
4. Sara Dellantonio

In Natural Language Processing

1. Marco Guerini
2. Serra Sinem Tekiroğlu
3. Helena Bonaldi
4. Sara Dellantonio

In Safety, Robustness & Interpretability

1. Marco Guerini
2. Serra Sinem Tekiroğlu
3. Helena Bonaldi
4. Sara Dellantonio

Citations in the corpus

• HateCheck: Functional Tests for Hate Speech Detection Models (2020, neutral): “Their performance matches SOTA results (Mozafari et al., 2019; Cao et al., 2020).”
• Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering (2022, methodology): “Many benchmarks for automatic HS detection are currently available (Mathew et al., 2021; Cao et al., 2020; Kumar et al., 2018; Hosseinmardi et al., 2015; Waseem, 2016; Burnap and Williams, 2016).”