@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://preview.aclanthology.org/moar-dois/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."
}