Deep learning based fusion approach for hate speech detection

Yanling Zhou, Yanyan Yang*, Han Liu, Xiufeng Liu, Nick Savage

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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In recent years, the increasing prevalence of hate speech in social media has been considered as a serious problem worldwide. Many governments and organizations have made significant investment in hate speech detection techniques, which have also attracted the attention of the scientific community. Although plenty of literature focusing on this issue is available, it remains difficult to assess the performances of each proposed method, as each has its own advantages and disadvantages. A general way to improve the overall results of classification by fusing the various classifiers results is a meaningful attempt. We first focus on several famous machine learning methods for text classification such as Embeddings from Language Models (ELMo), Bidirectional Encoder Representation from Transformers (BERT) and Convolutional Neural Network (CNN), and apply these methods to the data sets of the SemEval 2019 Task 5. We then adopt some fusion strategies to combine the classifiers to improve the overall classification performance. The results show that the accuracy and F1-score of the classification are significantly improved.

Original languageEnglish
Article number9139953
Pages (from-to)128923-128929
Number of pages7
JournalIEEE Access
Publication statusPublished - 14 Jul 2020


  • Bert
  • classifiers fusion
  • CNN
  • Hate speech
  • machine learning


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