ITC’s Participation at DIMEMEX: Data Augmentation using Generative AI for Better Detection of Hate Speech in Mexican Memes

This article presents the work done for the detection of inappropriate content, hate speech, or neither of them in Mexican memes within the DIMEMEX competition as part of IberLEF 2025. In contemporary society, the employment of memes as a medium for conveying ideas or messages has become a prevalent practice across a wide range of social networks utilized by users worldwide. The automatic detection of inappropriate content or hate speech has become a subject of significant interest for the scientific community. In this study, we propose an approach that utilizes paraphrasing to augment data, employing Transformers-based models for the classification of messages within memes. The proposal that is the subject of this study is a BETO-based model. This model obtained an f1-score of 0.52, which placed it in fourth place in the final phase for task 1. It is concluded that, despite the encouraging results, the task is quite complex. This conclusion is based on the analysis of the evaluated metrics, which revealed that all of the results fell below 0.60.