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A Deep Learning Approach for Automatic Hate Speech Detection in the Saudi Twittersphere
| Content Provider | MDPI |
|---|---|
| Author | Alshalan, Raghad Al-Khalifa, Hend |
| Copyright Year | 2020 |
| Description | With the rise of hate speech phenomena in the Twittersphere, significant research efforts have been undertaken in order to provide automatic solutions for detecting hate speech, varying from simple machine learning models to more complex deep neural network models. Despite this, research works investigating hate speech problem in Arabic are still limited. This paper, therefore, aimed to investigate several neural network models based on convolutional neural network (CNN) and recurrent neural network (RNN) to detect hate speech in Arabic tweets. It also evaluated the recent language representation model bidirectional encoder representations from transformers (BERT) on the task of Arabic hate speech detection. To conduct our experiments, we firstly built a new hate speech dataset that contained 9316 annotated tweets. Then, we conducted a set of experiments on two datasets to evaluate four models: CNN, gated recurrent units (GRU), CNN + GRU, and BERT. Our experimental results in our dataset and an out-domain dataset showed that the CNN model gave the best performance, with an F1-score of 0.79 and area under the receiver operating characteristic curve (AUROC) of 0.89. |
| Starting Page | 8614 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app10238614 |
| Journal | Applied Sciences |
| Issue Number | 23 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-12-01 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Cybernetical Science Abusive Language Arabic Arabic Tweets Hate Speech Detection |
| Content Type | Text |
| Resource Type | Article |
