Tong, Lei;
Liu, Zhihua;
Jiang, Zheheng;
Zhou, Feixiang;
Chen, Long;
Lyu, Jialin;
Zhang, Xiangrong;
... Zhou, Huiyu; + view all
(2022)
Cost-sensitive Boosting Pruning Trees for depression detection on Twitter.
IEEE Transactions on Affective Computing
p. 1.
10.1109/taffc.2022.3145634.
(In press).
Preview |
Text
Chen_Cost-sensitive Boosting Pruning Trees for depression detection on Twitter_AAM.pdf Download (9MB) | Preview |
Abstract
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 the 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 CBPT, we use additional three datasets from the UCI machine learning repository and CBPT obtains appealing classification results against several state of the arts boosting algorithms. Finally, we comprehensively explore the influence factors for the model prediction, and the results manifest that our proposed framework is promising for identifying Twitter users with depression.
| Type: | Article |
|---|---|
| Title: | Cost-sensitive Boosting Pruning Trees for depression detection on Twitter |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/taffc.2022.3145634 |
| Publisher version: | https://doi.org/10.1109/TAFFC.2022.3145634 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | data mining, boosting ensemble learning, online depression detection, online behaviours |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163778 |
Archive Staff Only
 |
View Item |
