@inproceedings{husunbeyi-etal-2022-identifying,
title = "Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques",
author = {H{\"u}s{\"u}nbeyi, Zehra Melce and
Akar, Didar and
{\"O}zg{\"u}r, Arzucan},
editor = "Adebayo, Kolawole and
Nanda, Rohan and
Verma, Kanishk and
Davis, Brian",
booktitle = "Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lateraisse-1.5",
pages = "32--41",
abstract = "Discriminatory language, in particular hate speech, is a global problem posing a grave threat to democracy and human rights. Yet, it is not always easy to identify, as it is rarely explicit. In order to detect hate speech, we developed Hierarchical Attention Network (HAN) based and Bidirectional Encoder Representations from Transformer (BERT) based deep learning models to capture the changing discursive cues and understand the context around the discourse. In addition, we designed linguistic features using critical discourse analysis techniques and integrated them into these neural network models. We studied the compatibility of our model with the hate speech detection problem by comparing it with traditional machine learning models, as well as a Convolution Neural Network (CNN) based model, a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based model which reached significant performance results for hate speech detection. Our results on a manually annotated corpus of print media in Turkish show that the proposed approach is effective for hate speech detection. We believe that the feature sets created for the Turkish language will encourage new studies in the quantitative analysis of hate speech.",
}
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<abstract>Discriminatory language, in particular hate speech, is a global problem posing a grave threat to democracy and human rights. Yet, it is not always easy to identify, as it is rarely explicit. In order to detect hate speech, we developed Hierarchical Attention Network (HAN) based and Bidirectional Encoder Representations from Transformer (BERT) based deep learning models to capture the changing discursive cues and understand the context around the discourse. In addition, we designed linguistic features using critical discourse analysis techniques and integrated them into these neural network models. We studied the compatibility of our model with the hate speech detection problem by comparing it with traditional machine learning models, as well as a Convolution Neural Network (CNN) based model, a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based model which reached significant performance results for hate speech detection. Our results on a manually annotated corpus of print media in Turkish show that the proposed approach is effective for hate speech detection. We believe that the feature sets created for the Turkish language will encourage new studies in the quantitative analysis of hate speech.</abstract>
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%0 Conference Proceedings
%T Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques
%A Hüsünbeyi, Zehra Melce
%A Akar, Didar
%A Özgür, Arzucan
%Y Adebayo, Kolawole
%Y Nanda, Rohan
%Y Verma, Kanishk
%Y Davis, Brian
%S Proceedings of the First Workshop on Language Technology and Resources for a Fair, Inclusive, and Safe Society within the 13th Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F husunbeyi-etal-2022-identifying
%X Discriminatory language, in particular hate speech, is a global problem posing a grave threat to democracy and human rights. Yet, it is not always easy to identify, as it is rarely explicit. In order to detect hate speech, we developed Hierarchical Attention Network (HAN) based and Bidirectional Encoder Representations from Transformer (BERT) based deep learning models to capture the changing discursive cues and understand the context around the discourse. In addition, we designed linguistic features using critical discourse analysis techniques and integrated them into these neural network models. We studied the compatibility of our model with the hate speech detection problem by comparing it with traditional machine learning models, as well as a Convolution Neural Network (CNN) based model, a Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU) based model which reached significant performance results for hate speech detection. Our results on a manually annotated corpus of print media in Turkish show that the proposed approach is effective for hate speech detection. We believe that the feature sets created for the Turkish language will encourage new studies in the quantitative analysis of hate speech.
%U https://aclanthology.org/2022.lateraisse-1.5
%P 32-41
Markdown (Informal)
[Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques](https://aclanthology.org/2022.lateraisse-1.5) (Hüsünbeyi et al., LATERAISSE 2022)
ACL