Citation

Angskun J, Tipprasert S, Angskun T. J. Big Data 2022; 9(1).

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1186/s40537-022-00622-2

PMID

unavailable

Abstract

During the coronavirus pandemic, the number of depression cases has dramatically increased. Several depression sufferers disclose their actual feeling via social media. Thus, big data analytics on social networks for real-time depression detection is proposed. This research work detected the depression by analyzing both demographic characteristics and opinions of Twitter users during a two-month period after having answered the Patient Health Questionnaire-9 used as an outcome measure. Machine learning techniques were applied as the detection model construction. There are five machine learning techniques explored in this research which are Support Vector Machine, Decision Tree, Naïve Bayes, Random Forest, and Deep Learning. The experimental results revealed that the Random Forest technique achieved higher accuracy than other techniques to detect the depression. This research contributes to the literature by introducing a novel model based on analyzing demographic characteristics and text sentiment of Twitter users. The model can capture depressive moods of depression sufferers. Thus, this work is a step towards reducing depression-induced suicide rates. © 2022, The Author(s).

Language: en

Keywords

Coronavirus; Social networks; Learning systems; Population statistics; Demographic characteristics; Social networking (online); Decision trees; Support vector machines; Social media; Learning algorithms; Random forests; Big data; Social network; Deep learning; Depression detection; Real- time; Data Analytics; Big data analytics; Machine learning techniques; Big data analytic; Coronaviruses; Data analytics