DEPRESSION DETECTION FROM SOCIAL NETWORK DATA USING ML

Abstract

Social networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos reflecting their moods, feelings and sentiments. This creates an opportunity to analyze social network data for user's feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools. This study used data from social media networks to explore various methods of early detection of depressive tweets based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects’ behavior based on different aspects of their writings: textual spreading, time gap, and time span. In this study, we aim to perform depression analysis on Twitter data collected from an online public source. To investigate the effect of depression detection, we propose machine learning technique as an efficient and scalable method. The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.