HOMO - MEX : A M exican S panish Annotated Corpus for LGBT +phobia Detection on T witter (2023)

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.

## introduction lgbt+phobia 2 is a global problem @xcite . among the consequences faced by the lgbt+ community are substance abuse disorders among its members (wallace and santacruz, 2017), 1 ## related work recent work explored using nlp to detect bullying, hate speech, violence, and aggressiveness. state-of-the-art models were developed for generalpurpose hate detection and hateful content directed at a particular group. for these models to be developed, large quantities of data are essential. recent data sets have emerged that seek to annotate hate towards marginalized groups, the majority being in english. although multi-language models and data sets exist, having language-specific models and data @xcite is demonstrably helpful. to the best of our knowledge, very few corpora have yet been developed to classify homophobic comments in spanish in online communication. we seek to bridge this gap. first, we will describe relevant work in nlp, more specifically, models that are used for sentiment analysis, and identification of harassment and hate. then, we present socially conscious work that seeks to be more inclusive and detect language discriminating against minority groups. finally, we discuss works that include spanish classification models. ## corpus for lgbt+phobia detection in mexican spanish this section describes the methodology for collecting data, our annotation process, and the challenges we faced. we also report the agreement between annotators. ## experiments on the homo-mex corpus for lgbt+phobia detection to evaluate the performance of various classifiers on our corpus, we performed several experiments using two main approaches: traditional machine learning methods and deep learning architectures. we describe these experiments in this section. the homo-mex corpus consists of two overlapped subsets. the first subset is comprised of those tweets that can be either "lgbt+phobic" (lp), "not lgbt+phobic" (nlp), and "irrelevant to the lgbt+ community" (i). on the other hand, the second subset contains the lgbt+phobic tweets that were multi-labeled as "lesbophobic" (l), "gayphobic" (g), "biphobic" (b), "transphobic" (t), and "other" (o). for conciseness, we will refer to the first subset as "lgbt+phobia detection", and the second as "fine-grained classification". both lgbt+phobia detection and finegrained classification subsets were split into train and test partitions. the resulting size and distribution of labels in each partition are shown in tables 3 and 4 . in ## results and discussion we performed classification experiments using naive bayes, svm, logistic regression, and random forest classifiers. lgbt+phobia detection and fine-grained classification subsets. the results of these experiments can be observed in lgbt+phobic speech in mexican spanish.