Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores
This work addresses early diagnosis of depression for young adults, but it appears incremental as it builds on existing datasets and methods.
The paper tackled early depression detection from Reddit social media text using the eRisk 2018 dataset, achieving good results by leveraging topic analysis and learned confidence scores.
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.