A Cost-aware Study of Depression Language on Social Media using Topic and Affect Contextualization
This work addresses depression detection for mental health applications, but it is incremental as it builds on existing methods with added energy analysis.
The paper tackled depression detection on social media by proposing an ensemble learning system with topic and affective contextualization, which improved classification and showed that Transformers can increase F-score by 2% but raise energy costs a hundredfold.
Depression is a growing issue in society's mental health that affects all areas of life and can even lead to suicide. Fortunately, prevention programs can be effective in its treatment. In this context, this work proposes an automatic system for detecting depression on social media based on machine learning and natural language processing methods. This paper presents the following contributions: (i) an ensemble learning system that combines several types of text representations for depression detection, including recent advances in the field; (ii) a contextualization schema through topic and affective information; (iii) an analysis of models' energy consumption, establishing a trade-off between classification performance and overall computational costs. To assess the proposed models' effectiveness, a thorough evaluation is performed in two datasets that model depressive text. Experiments indicate that the proposed contextualization strategies can improve the classification and that approaches that use Transformers can improve the overall F-score by 2% while augmenting the energy cost a hundred times. Finally, this work paves the way for future energy-wise systems by considering both the performance classification and the energy consumption.