Social Media-Based Psychological Insights for Detecting Depression Using Comparative Analysis of Machine Learning Algorithm

Abstract

People's communication methods are changing due to rapid advancements in technology. As the Internet has grown, social media platforms like Facebook, Instagram, Telegram, and Twitter have gained prominence as places for people to express their feelings, ideas, and psychological patterns. Millions of people worldwide suffer from depression, a common and devastating mental illness. Effective intervention and therapy depend on early discovery and precise diagnosis. With an emphasis on both conventional methods like clinical interviews and standardized tests as well as more recent approaches that use machine learning and natural language processing (NLP), this study examines a variety of psychological analysis tools for diagnosing depression. Through text analysis, psychological analysis gleans facts, features, and significant information from user perspectives. Social networks are used by psychological analytic researchers to identify activity and behavior associated with sadness. Social networks convey a wealth of information on the attitudes and behaviors associated with the onset of depression, including low sociability, medical treatment, self-centeredness, and a high level of daytime and nighttime activity. The study emphasizes how behavioral indicators, speech patterns, facial expressions, and textual analysis from online interactions or patient interviews can be used to detect signs of depression. The project intends to provide a thorough framework for identifying depression by evaluating psychological data using both qualitative and quantitative methods. This could increase diagnostic precision and result in more individualized treatment regimens. The effectiveness and accessibility of mental health care could be greatly improved by incorporating these strategies into clinical practice. In order to detect depression in tweets, we employed five machine learning classifiers in this research: logistic regression, K-nearest neighbor, support vector machines, decision trees, and LSTM. Technically, the oversampling of approaches is examined in two types of datasets: balanced and imbalanced. The findings indicate that for both balanced and imbalanced information, the LSTM classification model performs better than the other baseline models in the healthcare approach to depression being recognized.