Detecting Hate Speech on Twitter Using a Convolution-GRU Based Deep Neural Network

Abstract

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective countermeasures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13 percents in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.

A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media

“…Gambck et al [8] proposed a hate speech classifier based on CNN model trained on different feature embeddings such as word embeddings and character n-grams. Zhang et al [25] used a CNN+GRU (Gated Recurrent Unit network) neural network model initialized with pre-trained word2vec embeddings to capture both word/character combinations (e. g., n-grams, phrases) and word/character dependencies (order information). Waseem et al [23] brought a new insight to hate speech and abusive language detection tasks by proposing a multi-task learning framework to deal with datasets across different annotation schemes, labels, or geographic and cultural influences from data sampling.…”

“…To detect online hate speech, a large number of scientific studies have been dedicated by using Natural Language Processing (NLP) in combination with Machine Learning (ML) and Deep Learning (DL) methods [1,8,11,13,22,25]. Although supervised machine learning-based approaches have used different text mining-based features such as surface features, sentiment analysis, lexical resources, linguistic features, knowledge-based features or user-based and platformbased metadata [3,6,23], they necessitate a well-defined feature extraction approach.…”

Towards countering hate speech against journalists on social media

“…CNN + GRU. Work in [32] added a GRU layer followed by a global max pooling layer on top of CNN model. The GRU layer captures sequence feature relations and learns to identify dependencies between n-gram features LSTM.…”

“…The second approach takes the average of the Precision, Recall and F1 on different classes. Existing studies on hate speech detection have primarily reported their results using micro-average Precision, Recall and F1 [29,28,5,15,32].…”

Attention-Based Method for Categorizing Different Types of Online Harassment Language

“…Moreover, in the presented text augmentation approach, the number of tweets in each class remains the same, but their words are augmented with words extracted from their ConceptNet relations and their description extracted from Wikidata. Zhang et al [7] combined convolutional and gated recurrent networks to detect hate speech in tweets. Others have proposed different methods, which are not based on deep learning.…”