Synergizing Digital Twin Technology for Advanced Depression Categorization in Social-Media through Data Mining Analysis

Sajawal Khan; Muhammad Dawood khan; Muhammad Awais Ashraf; Aamir Munir; Muhammad Zunair Zamir

Authors

Sajawal Khan School of Information Engineering, Chang’an University, Xi'an, Shaanxi Province, China
Muhammad Dawood khan Computer Science at Layyah Campus GCUF, Pakistan
Muhammad Awais Ashraf School of Information Engineering, Chang’an University, Xi'an, Shaanxi Province, China
Aamir Munir School of Information Engineering, Chang’an University, Xi'an, Shaanxi Province, China
Muhammad Zunair Zamir School of Information Engineering, Chang’an University, Xi'an, Shaanxi Province, China

Keywords:

Digital twin technology, Data Mining, Sentiment Analysis, Social Media, Depression, Categorization.

Abstract

The progression from negative emotions to depression is a significant concern, marked by persistent sadness and an inability to cope with challenging circumstances. Regrettably, it can lead to the extreme step of suicide. According to the World Health Organization (WHO), 4.4% of the global population currently grapples with depression. Shockingly, 700,000 individuals worldwide took their own lives in 2023, and this tragic number continues to escalate. Our objective is to detect signs of depression in individuals through their social media posts, SMS, or comments. We collected nearly 10,000 pieces of information from Twitter comments, Facebook posts, and remarks. Employing data mining and machine learning algorithms has proven instrumental in swiftly discerning individuals' emotional states. To predict depression versus non-depression, we employed six classifiers, with support vector machines (SVMs) demonstrating the highest accuracy. A comparison between SVM and Naïve Bayes revealed that Naïve Bayes yielded superior results in our study.

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