I2C-UHU at MentalRiskES 2023: Detecting and Identifying Mental Disorder Risks in Social Media using Transformer-Based Models

Abstract

This paper presents the approaches proposed by the I2C Group to address MentalRiskES: Early Detection of Mental Disorder Risks in Spanish, as part of IberLEF 2023. Our proposal involves developing distinct transformer-based classifiers to tackle three specific tasks: i) Task1a: Binary classification for the detection of eating disorders, ii) Task1b: Simple regression for the detection of eating disorders, and iii) Task2c: Multiclass classification for the detection of depression. The main approach consisted of fine-tuning pre-trained transformer-based models. For the binary tasks, diverse methodologies were employed to predict users based on the predictions obtained from their individual messages. For the multiclass task, data augmentation approaches were used to balance the minority classes messages. The final submitted predictions achieved a Macro-F1 score of 0.641 for Task1a, ranking 19th out of 22 participants; an RMSE of 0.24 for Task1b, ranking 4th out of 17 participants; and a Macro-F1 score of 0.232 for Task2c, ranking 4th out of 10 participants.