Psychological Analysis for Depression Detection from Social Networking Sites

Area of Science:

• Computational linguistics
• Psychological informatics
• Machine learning in healthcare

Background:

• Social media platforms are increasingly used for sharing personal thoughts and emotions.
• Psychological analysis of online text aids in understanding user behavior and mental states.
• Social networks offer rich data for detecting depression-related activities and mindset shifts.

Purpose of the Study:

• To evaluate machine learning classifiers for detecting depression from social media text.
• To compare the performance of various models on both balanced and imbalanced datasets.
• To identify the most effective model for depression detection in tweets.

Main Methods:

• Utilized five machine learning classifiers: decision trees, K-nearest neighbor, support vector machines, logistic regression, and Long Short-Term Memory (LSTM).
• Collected and analyzed datasets in both balanced and imbalanced forms, investigating oversampling techniques.
• Focused on analyzing user-generated content from social media for depression-related behavioral patterns.

Main Results:

• The Long Short-Term Memory (LSTM) classification model demonstrated superior performance compared to baseline models.
• LSTM achieved higher accuracy in depression detection on both balanced and imbalanced datasets.
• The study confirmed LSTM's efficacy in the context of healthcare approaches for mental health analysis.

Conclusions:

• Machine learning, particularly LSTM, shows significant promise for automated depression detection using social media data.
• The effectiveness of LSTM in handling imbalanced data is crucial for real-world mental health applications.
• Social media analysis presents a viable avenue for early identification and intervention in depression.