@inproceedings{abitte-kanta-etal-2024-selam,
title = "Selam@{D}ravidian{L}ang{T}ech 2024:Identifying Hate Speech and Offensive Language",
author = "Abitte Kanta, Selam and
Sidorov, Grigori and
Gelbukh, Alexander",
editor = "Chakravarthi, Bharathi Raja and
Priyadharshini, Ruba and
Madasamy, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth and
Nadarajan, Rajeswari and
Ravikiran, Manikandan",
booktitle = "Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.dravidianlangtech-1.14",
pages = "91--95",
abstract = "Social media has transformed into a powerful tool for sharing information while upholding the principle of free expression. However, this open platform has given rise to significant issues like hate speech, cyberbullying, aggression, and offensive language, negatively impacting societal well-being. These problems can even lead to severe consequences such as suicidal thoughts, affecting the mental health of the victims. Our primary goal is to develop an automated system for the rapid detection of offensive content on social media, facilitating timely interventions and moderation. This research employs various machine learning classifiers, utilizing character N-gram TF-IDF features. Additionally, we introduce SVM, RL, and Convolutional Neural Network (CNN) models specifically designed for hate speech detection. SVM utilizes character Ngram TF-IDF features, while CNN employs word embedding features. Through extensive experiments, we achieved optimal results, with a weighted F1-score of 0.77 in identifying hate speech and offensive language.",
}
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<abstract>Social media has transformed into a powerful tool for sharing information while upholding the principle of free expression. However, this open platform has given rise to significant issues like hate speech, cyberbullying, aggression, and offensive language, negatively impacting societal well-being. These problems can even lead to severe consequences such as suicidal thoughts, affecting the mental health of the victims. Our primary goal is to develop an automated system for the rapid detection of offensive content on social media, facilitating timely interventions and moderation. This research employs various machine learning classifiers, utilizing character N-gram TF-IDF features. Additionally, we introduce SVM, RL, and Convolutional Neural Network (CNN) models specifically designed for hate speech detection. SVM utilizes character Ngram TF-IDF features, while CNN employs word embedding features. Through extensive experiments, we achieved optimal results, with a weighted F1-score of 0.77 in identifying hate speech and offensive language.</abstract>
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%0 Conference Proceedings
%T Selam@DravidianLangTech 2024:Identifying Hate Speech and Offensive Language
%A Abitte Kanta, Selam
%A Sidorov, Grigori
%A Gelbukh, Alexander
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%Y Nadarajan, Rajeswari
%Y Ravikiran, Manikandan
%S Proceedings of the Fourth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julian’s, Malta
%F abitte-kanta-etal-2024-selam
%X Social media has transformed into a powerful tool for sharing information while upholding the principle of free expression. However, this open platform has given rise to significant issues like hate speech, cyberbullying, aggression, and offensive language, negatively impacting societal well-being. These problems can even lead to severe consequences such as suicidal thoughts, affecting the mental health of the victims. Our primary goal is to develop an automated system for the rapid detection of offensive content on social media, facilitating timely interventions and moderation. This research employs various machine learning classifiers, utilizing character N-gram TF-IDF features. Additionally, we introduce SVM, RL, and Convolutional Neural Network (CNN) models specifically designed for hate speech detection. SVM utilizes character Ngram TF-IDF features, while CNN employs word embedding features. Through extensive experiments, we achieved optimal results, with a weighted F1-score of 0.77 in identifying hate speech and offensive language.
%U https://aclanthology.org/2024.dravidianlangtech-1.14
%P 91-95
Markdown (Informal)
[Selam@DravidianLangTech 2024:Identifying Hate Speech and Offensive Language](https://aclanthology.org/2024.dravidianlangtech-1.14) (Abitte Kanta et al., DravidianLangTech-WS 2024)
ACL