Big data analytics on social networks for real-time depression detection

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, Naive 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.

Automated summary

This study proposes a real-time depression detection method based on big data analytics by analyzing demographic characteristics and opinions of social media users. Machine learning techniques were used to construct the detection model, with the Random Forest technique achieving higher accuracy in detecting depression.