@inproceedings{madera-espindola-etal-2025-detecting,
title = "Detecting Sexism in Tweets: A Sentiment Analysis and Graph Neural Network Approach",
author = "Madera-Esp{\'i}ndola, Diana P. and
Caballero-Dom{\'i}nguez, Zoe and
Ram{\'i}rez-Mac{\'i}as, Valeria J. and
Butt, Sabur and
Ceballos, Hector",
editor = "Ebrahimi, Abteen and
Haider, Samar and
Liu, Emmy and
Haider, Sammar and
Leonor Pacheco, Maria and
Wein, Shira",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)",
month = apr,
year = "2025",
address = "Albuquerque, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-srw.5/",
doi = "10.18653/v1/2025.naacl-srw.5",
pages = "48--54",
ISBN = "979-8-89176-192-6",
abstract = "In the digital age, social media platforms like Twitter serve as an extensive repository of public discourse, including instances of sexism. It is important to identify such behavior since radicalized ideologies can lead to real-world violent acts. This project aims to develop a deep learning-based tool that leverages a combination of BERT (both English and multilingual versions) and GraphSAGE, a Graph Neural Network (GNN) model, alongside sentiment analysis and natural language processing (NLP) techniques. The tool is designed to analyze tweets for sexism detection and classify them into five categories."
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%0 Conference Proceedings
%T Detecting Sexism in Tweets: A Sentiment Analysis and Graph Neural Network Approach
%A Madera-Espíndola, Diana P.
%A Caballero-Domínguez, Zoe
%A Ramírez-Macías, Valeria J.
%A Butt, Sabur
%A Ceballos, Hector
%Y Ebrahimi, Abteen
%Y Haider, Samar
%Y Liu, Emmy
%Y Haider, Sammar
%Y Leonor Pacheco, Maria
%Y Wein, Shira
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, USA
%@ 979-8-89176-192-6
%F madera-espindola-etal-2025-detecting
%X In the digital age, social media platforms like Twitter serve as an extensive repository of public discourse, including instances of sexism. It is important to identify such behavior since radicalized ideologies can lead to real-world violent acts. This project aims to develop a deep learning-based tool that leverages a combination of BERT (both English and multilingual versions) and GraphSAGE, a Graph Neural Network (GNN) model, alongside sentiment analysis and natural language processing (NLP) techniques. The tool is designed to analyze tweets for sexism detection and classify them into five categories.
%R 10.18653/v1/2025.naacl-srw.5
%U https://aclanthology.org/2025.naacl-srw.5/
%U https://doi.org/10.18653/v1/2025.naacl-srw.5
%P 48-54
Markdown (Informal)
[Detecting Sexism in Tweets: A Sentiment Analysis and Graph Neural Network Approach](https://aclanthology.org/2025.naacl-srw.5/) (Madera-Espíndola et al., NAACL 2025)
ACL
- Diana P. Madera-Espíndola, Zoe Caballero-Domínguez, Valeria J. Ramírez-Macías, Sabur Butt, and Hector Ceballos. 2025. Detecting Sexism in Tweets: A Sentiment Analysis and Graph Neural Network Approach. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), pages 48–54, Albuquerque, USA. Association for Computational Linguistics.