A Framework for Identifying Depression on Social Media: MentalRiskES@IberLEF 2023

TL;DR

This paper presents a machine learning framework combining traditional and deep learning methods to predict depression likelihood from social media data, demonstrating effective approaches for multiple predictive subtasks.

Contribution

The paper introduces a novel approach that leverages BERT embeddings and regression techniques to improve depression prediction from social media conversations.

Findings

Using BERT embeddings with linear regression outperforms fine-tuning BERT directly.

The framework effectively predicts depression likelihood across multiple subtasks.

Code implementation is publicly available for reproducibility.

Abstract

This paper describes our participation in the MentalRiskES task at IberLEF 2023. The task involved predicting the likelihood of an individual experiencing depression based on their social media activity. The dataset consisted of conversations from 175 Telegram users, each labeled according to their evidence of suffering from the disorder. We used a combination of traditional machine learning and deep learning techniques to solve four predictive subtasks: binary classification, simple regression, multiclass classification, and multi-output regression. We approached this by training a model to solve the multi-output regression case and then transforming the predictions to work for the other three subtasks. We compare the performance of two modeling approaches: fine-tuning a BERT-based model directly for the task or using its embeddings as inputs to a linear regressor, with the latter yielding better results. The code to reproduce our results can be found at: https://github.com/simonsanvil/EarlyDepression-MentalRiskES