Skip to main content

Cost-sensitive Boosting Pruning Trees for depression detection on Twitter

Tong, Lei, Liu, Zhihua, Jiang, Zheheng, Zhou, Feixiang, Chen, Long, Lyu, Jialin, Zhang, Xiangrong, Zhang, Qianni, Sadka, Abdul, Wang, Yinhai, and others. (2022) Cost-sensitive Boosting Pruning Trees for depression detection on Twitter. IEEE Transactions on Affective Computing, . ISSN 2371-9850. (doi:10.1109/TAFFC.2022.3145634) (KAR id:93004)

PDF Author's Accepted Manuscript
Language: English
Download (4MB) Preview
[thumbnail of Cost-sensitive_Boosting_Pruning_Trees_for_depression_detection_on_Twitter.pdf]
Preview
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL
https://doi.org/10.1109/TAFFC.2022.3145634

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 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

Item Type: Article
DOI/Identification number: 10.1109/TAFFC.2022.3145634
Uncontrolled keywords: data mining, boosting ensemble learning, online depression detection, online behaviours
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Caroline Li
Date Deposited: 17 Feb 2022 23:06 UTC
Last Modified: 18 Mar 2022 16:17 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/93004 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year