Effective hate-speech detection in Twitter data using recurrent neural networks
By Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth. 2018
255 citations recorded by the source corpus.
This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users’ tendency towards racism or sexism. This data is fed as input to the above classifiers along with the word frequency vectors derived from the textual content. We evaluate our approach on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state-of-the-art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.
Subfields: Artificial Intelligence, Social Psychology, Natural Language Processing, Safety, Robustness & Interpretability, Signal Processing
How researchers see this paper
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In Artificial Intelligence
- Yulia Tsvetkov
- Vidhisha Balachandran
- Sachin Kumar
- Antonios Anastasopoulos
- Lucille Njoo
- Diyi Yang
- Jeremy Blackburn
- Antigoni-Maria Founta
In Social Psychology
In Natural Language Processing
- Yulia Tsvetkov
- Sachin Kumar
- Vidhisha Balachandran
- Antonios Anastasopoulos
- Lucille Njoo
- Diyi Yang
- Jeremy Blackburn
- Athena Vakali
In Safety, Robustness & Interpretability
- Sachin Kumar
- Vidhisha Balachandran
- Lucille Njoo
- Antonios Anastasopoulos
- Diyi Yang
- Bernard Jim Jansen
- Jeremy Blackburn
- Ayanna M. Howard
In Signal Processing
- Vidhisha Balachandran
- Bernard Jim Jansen
- Yulia Tsvetkov
- Soon-gyo Jung
- Diyi Yang
- Hind Almerekhi
- Joni O. Salminen
- Antonios Anastasopoulos
Citations in the corpus
- Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, methodology): “Neural Detection Toxicity Supervised: (Gambäck and Sikdar, 2017; Pitsilis et al., 2018; d’Sa et al., 2020; Xiang et al., 2021); Semi- and Unsupervised: (Korzeniowski et al.”
- Studying Generalisability across Abusive Language Detection Datasets (2019, background): “Deep Neural networks such as Convolutional Neural Networks, CNN (Zhang et al., 2018; Gamb¨ack and Sikdar, 2017) and variations of Recurrent Neural Networks, RNN (Pitsilis et al., 2018; Gao and Huang, 2017) have seen widespread success, regularly obtaining state-of-the-art results on various datasets”
- Online Hate Interpretation Varies by Country, But More by Individual: A Statistical Analysis Using Crowdsourced Ratings (2018, background): “Different approaches have been devised by researchers to detect hateful social media comments, such as dictionary-based [8], distributional semantics [2], multi-feature [5], [21] and neural networks [22], [23].”
- Exploring the Role of Grammar and Word Choice in Bias Toward African American English (AAE) in Hate Speech Classification (2022, methodology): “These and other deep learning models for this task mostly rely on a few popular approaches including Convolutional Neural Nets [1], Recurrent Neural Nets [30], Bi-LSTM [33], and word embeddings (ie BERT, Word2Vec, and TF-IDF Vectorizor), many of which combine these techniques [1].”
- A Unified Deep Learning Architecture for Abuse Detection (2018, background): “Finally, authors in [30], based on an ensemble LSTM setup and a word-based frequency vectorization, focus on detecting racism, sexism, and neutral Twitter data.”