# Improved two-stage hate speech classification for twitter based on Deep Neural Networks

arXiv: https://arxiv.org/abs/2206.04162 · HTML: https://mlsanity.com/paper/2206.04162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 64 references that are in the map.
- First posted on arXiv 8 Jun 2022.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Train longer, generalize better: closing the generalization gap in large batch training of neural networks](https://mlsanity.com/paper/1705.08741/) (2017)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Deceiving Google's Perspective API Built for Detecting Toxic Comments](https://mlsanity.com/paper/1702.08138/) (2017)
- [One-step and Two-step Classification for Abusive Language Detection on Twitter](https://mlsanity.com/paper/1706.01206/) (2017)
- [Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms](https://mlsanity.com/paper/1801.06482/) (2018)
- [Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection](https://mlsanity.com/paper/1804.03124/) (2018)
- [Detecting Offensive Language in Tweets Using Deep Learning](https://mlsanity.com/paper/1801.04433/) (2018)
- [Neural Character-based Composition Models for Abuse Detection](https://mlsanity.com/paper/1809.00378/) (2018)
- [Automatic Detection of Sexist Statements Commonly Used at the Workplace](https://mlsanity.com/paper/2007.04181/) (2020)
- [Neural Word Decomposition Models for Abusive Language Detection](https://mlsanity.com/paper/1910.01043/) (2019)
- [UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks](https://mlsanity.com/paper/1904.07839/) (2019)
- [Identifying Offensive Posts and Targeted Offense from Twitter](https://mlsanity.com/paper/1904.09072/) (2019)
- [LSTMs with Attention for Aggression Detection](https://mlsanity.com/paper/1807.06151/) (2018)
- [NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier](https://mlsanity.com/paper/1904.05152/) (2019)
- [UBC-NLP at SemEval-2019 Task 6:Ensemble Learning of Offensive Content With Enhanced Training Data](https://mlsanity.com/paper/1906.03692/) (2019)
- [Attention-based method for categorizing different types of online harassment language](https://mlsanity.com/paper/1909.13104/) (2019)

## Abstract (arXiv)

Hate speech is a form of online harassment that involves the use of abusive language, and it is commonly seen in social media posts. This sort of harassment mainly focuses on specific group characteristics such as religion, gender, ethnicity, etc and it has both societal and economic consequences nowadays. The automatic detection of abusive language in text postings has always been a difficult task, but it is lately receiving much interest from the scientific community. This paper addresses the important problem of discerning hateful content in social media. The model we propose in this work is an extension of an existing approach based on LSTM neural network architectures, which we appropriately enhanced and fine-tuned to detect certain forms of hatred language, such as racism or sexism, in a short text. The most significant enhancement is the conversion to a two-stage scheme consisting of Recurrent Neural Network (RNN) classifiers. The output of all One-vs-Rest (OvR) classifiers from the first stage are combined and used to train the second stage classifier, which finally determines the type of harassment. Our study includes a performance comparison of several proposed alternative methods for the second stage evaluated on a public corpus of 16k tweets, followed by a generalization study on another dataset. The reported results show the superior classification quality of the proposed scheme in the task of hate speech detection as compared to the current state-of-the-art.