Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores

TL;DR

This paper presents a novel approach for early depression detection from Reddit social media texts by utilizing topic analysis and learned confidence scores, aiming to improve prevention and early diagnosis.

Contribution

It introduces the use of learned confidence scores combined with topic analysis for early depression detection from social media texts, advancing prior methods.

Findings

Achieved state-of-the-art results on the eRisk 2018 dataset.

Demonstrated the effectiveness of confidence scores in guiding decision-making.

Improved early detection accuracy compared to previous approaches.

Abstract

Computational research on mental health disorders from written texts covers an interdisciplinary area between natural language processing and psychology. A crucial aspect of this problem is prevention and early diagnosis, as suicide resulted from depression being the second leading cause of death for young adults. In this work, we focus on methods for detecting the early onset of depression from social media texts, in particular from Reddit. To that end, we explore the eRisk 2018 dataset and achieve good results with regard to the state of the art by leveraging topic analysis and learned confidence scores to guide the decision process.