Major Depression Disorder (MDD) is a mental condition that causes severe impairments in a person's life. Early detection of such a condition is imperative, especially to detect cases where the person does not even know that they are affected by it. Social media provide a ubiquitous platform with self-generated data that Machine Learning models enhanced with Natural Language Processing mechanisms can extensively explore. Nonetheless, we advocate that previous Machine Learning approaches to early detection of MDD do not adequately model the problem at hand. Most assume that the isolated publications on social media are enough for early detection. This paper proposes a new problem formulation for this task using the multiple-instance learning paradigm. Furthermore, we include a theoretical and experimental analysis of the method on a dataset of Brazilian university students using the transformer and LSTM architectures. The results indicate that the proposed approach yields better accuracy and explanation capabilities than previous studies that do not rely on multiple-instance learning.

Depression has increased at alarming rates in the worldwide population. One alternative to finding depressed individuals is using social media data to train machine learning (ML) models to identify depressed cases automatically. Previous works have already relied on ML to solve this task with reasonably good F-measure scores. Still, several limitations prevent the full potential of these models. In this work, we show that the depression identification task through social media is better modeled as a Multiple Instance Learning (MIL) problem that can exploit the temporal dependencies between posts.