An Analysis of the Impact of Gender and Age on Perceiving and Identifying Sexist Posts

This research addresses the challenge of detecting sexism in Spanish-language tweets on social media. Our analysis explores labeling differences among annotators with diverse sociodemographic attributes, emphasizing their relevance in automated model development. Using a dataset enriched with labels from six diverse profiles, our study revealed nuanced perceptions of sexism across different genders and age ranges. Although there is considerable agreement between genders, instances of disagreement persist. Similarly, while there is a better consensus in terms of age, disagreements still arise. We use a RoBERTuito model fine-tuned on sexism identification, reaching an F1-score of 0.856 when training the model considering only the labels of the oldest age profile. These instances underscore the necessity for continuous model refinement to effectively capture subtle language variations.