Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ANVD7LEEC" target="_blank" >RIV/00216208:11320/22:NVD7LEEC - isvavai.cz</a>

Result on the web

<a href="https://aclanthology.org/2022.lateraisse-1.5" target="_blank" >https://aclanthology.org/2022.lateraisse-1.5</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Identifying Hate Speech Using Neural Networks and Discourse Analysis Techniques

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

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

ISBN

978-2-493-81409-8

ISSN

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e-ISSN

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Number of pages

10

Pages from-to

32-41

Publisher name

European Language Resources Association

Place of publication

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Event location

Marseille, France

Event date

Jan 1, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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