Cost-sensitive Boosting Pruning Trees for depression detection on Twitter
This work addresses the problem of early depression detection for mental health monitoring, but it is incremental as it builds on existing boosting methods with a new pruning approach.
The paper tackles early depression detection by mining social media behavior, proposing a novel classifier called Cost-sensitive Boosting Pruning Trees (CBPT) that achieves strong classification results on Twitter datasets and outperforms state-of-the-art boosting algorithms on additional UCI datasets.
Depression is one of the most common mental health disorders, and a large number of depressed people commit suicide each year. Potential depression sufferers usually do not consult psychological doctors because they feel ashamed or are unaware of any depression, which may result in severe delay of diagnosis and treatment. In the meantime, evidence shows that social media data provides valuable clues about physical and mental health conditions. In this paper, we argue that it is feasible to identify depression at an early stage by mining online social behaviours. Our approach, which is innovative to the practice of depression detection, does not rely on the extraction of numerous or complicated features to achieve accurate depression detection. Instead, we propose a novel classifier, namely, Cost-sensitive Boosting Pruning Trees (CBPT), which demonstrates a strong classification ability on two publicly accessible Twitter depression detection datasets. To comprehensively evaluate the classification capability of the CBPT, we use additional three datasets from the UCI machine learning repository and the CBPT obtains appealing classification results against several state of the arts boosting algorithms. Finally, we comprehensively explore the influence factors of model prediction, and the results manifest that our proposed framework is promising for identifying Twitter users with depression.