@inproceedings{vasquez-etal-2023-homo,
    title = "{HOMO}-{MEX}: A {M}exican {S}panish Annotated Corpus for {LGBT}+phobia Detection on {T}witter",
    author = "V{\'a}squez, Juan  and
      Andersen, Scott  and
      Bel-enguix, Gemma  and
      G{\'o}mez-adorno, Helena  and
      Ojeda-trueba, Sergio-luis",
    editor = {Chung, Yi-ling  and
      R{{\textbackslash}"ottger}, Paul  and
      Nozza, Debora  and
      Talat, Zeerak  and
      Mostafazadeh Davani, Aida},
    booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.woah-1.20",
    doi = "10.18653/v1/2023.woah-1.20",
    pages = "202--214",
    abstract = "In the past few years, the NLP community has actively worked on detecting LGBT+Phobia in online spaces, using textual data publicly available Most of these are for the English language and its variants since it is the most studied language by the NLP community. Nevertheless, efforts towards creating corpora in other languages are active worldwide. Despite this, the Spanish language is an understudied language regarding digital LGBT+Phobia. The only corpus we found in the literature was for the Peninsular Spanish dialects, which use LGBT+phobic terms different than those in the Mexican dialect. For this reason, we present Homo-MEX, a novel corpus for detecting LGBT+Phobia in Mexican Spanish. In this paper, we describe our data-gathering and annotation process. Also, we present a classification benchmark using various traditional machine learning algorithms and two pre-trained deep learning models to showcase our corpus classification potential.",
}

In the past few years, the NLP community has actively worked on detecting LGBT+Phobia in online spaces, using textual data publicly available Most of these are for the English language and its variants since it is the most studied language by the NLP community. Nevertheless, efforts towards creating corpora in other languages are active worldwide. Despite this, the Spanish language is an understudied language regarding digital LGBT+Phobia. The only corpus we found in the literature was for the Peninsular Spanish dialects, which use LGBT+phobic terms different than those in the Mexican dialect. For this reason, we present Homo-MEX, a novel corpus for detecting LGBT+Phobia in Mexican Spanish. In this paper, we describe our data-gathering and annotation process. Also, we present a classification benchmark using various traditional machine learning algorithms and two pre-trained deep learning models to showcase our corpus classification potential.
V{\'a}squez, Juan Andersen, Scott Bel-enguix, Gemma G{\'o}mez-adorno, Helena Ojeda-trueba, Sergio-luis
HOMO-MEX: A Mexican Spanish Annotated Corpus for LGBT+phobia Detection on Twitter