@inproceedings{ghosh-chowdhury-etal-2019-arhnet,
title = "{ARHN}et - Leveraging Community Interaction for Detection of Religious Hate Speech in {A}rabic",
author = "Ghosh Chowdhury, Arijit and
Didolkar, Aniket and
Sawhney, Ramit and
Shah, Rajiv Ratn",
editor = "Alva-Manchego, Fernando and
Choi, Eunsol and
Khashabi, Daniel",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-2038",
doi = "10.18653/v1/P19-2038",
pages = "273--280",
abstract = "The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language. Religious Hate Speech, in particular, often leads to unrest and sometimes aggravates to violence against people on the basis of their religious affiliations. The richness of the Arabic morphology and the limited available resources makes this task especially challenging. The current state-of-the-art approaches to detect hate speech in Arabic rely entirely on textual (lexical and semantic) cues. Our proposed methodology contends that leveraging Community-Interaction can better help us profile hate speech content on social media. Our proposed ARHNet (Arabic Religious Hate Speech Net) model incorporates both Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech.",
}
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<abstract>The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language. Religious Hate Speech, in particular, often leads to unrest and sometimes aggravates to violence against people on the basis of their religious affiliations. The richness of the Arabic morphology and the limited available resources makes this task especially challenging. The current state-of-the-art approaches to detect hate speech in Arabic rely entirely on textual (lexical and semantic) cues. Our proposed methodology contends that leveraging Community-Interaction can better help us profile hate speech content on social media. Our proposed ARHNet (Arabic Religious Hate Speech Net) model incorporates both Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech.</abstract>
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%0 Conference Proceedings
%T ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic
%A Ghosh Chowdhury, Arijit
%A Didolkar, Aniket
%A Sawhney, Ramit
%A Shah, Rajiv Ratn
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F ghosh-chowdhury-etal-2019-arhnet
%X The rapid widespread of social media has lead to some undesirable consequences like the rapid increase of hateful content and offensive language. Religious Hate Speech, in particular, often leads to unrest and sometimes aggravates to violence against people on the basis of their religious affiliations. The richness of the Arabic morphology and the limited available resources makes this task especially challenging. The current state-of-the-art approaches to detect hate speech in Arabic rely entirely on textual (lexical and semantic) cues. Our proposed methodology contends that leveraging Community-Interaction can better help us profile hate speech content on social media. Our proposed ARHNet (Arabic Religious Hate Speech Net) model incorporates both Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech.
%R 10.18653/v1/P19-2038
%U https://aclanthology.org/P19-2038
%U https://doi.org/10.18653/v1/P19-2038
%P 273-280
Markdown (Informal)
[ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic](https://aclanthology.org/P19-2038) (Ghosh Chowdhury et al., ACL 2019)
ACL