Depression Detection From Social Network Users

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 analyse social network data for user’s feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools. Mental health issues are widely accepted as one of the most prominent health challenges in the world, with over 300 million people currently suffering from depression alone. With massive volumes of user-generated data on social networking platforms, researchers are increasingly using machine learning to determine whether this content can be used to detect mental health problems in users. This study aims to develop a model to classify users with depression via machine learning techniques, which can learn from user-level labels to identify post-level labels .By combining every possibility of posts label category, it can generate temporal data which can then be used to classify users with depression. This project shows that there are clear differences in posting patterns between users with depression and non-depression, which is represented through the combined likelihood of posts label category.

Keywords: Depression, Social Network, Feelings, Analyse, Detection.