Predicting Depression Using Social Media Posts

Abstract

The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people's mental health status. The use of the UGC could aid in the prediction of mental health, particularly depression, where it is a significant medical condition that impairs one's ability to work, learn, eat, sleep, and enjoy life. However, all information about a person's mood and negativism can be gathered from their SNS user profile. Therefore, this study utilizes SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models is evaluated to classify the UGC: Decision Forest, Neural Network, and Support Vector Machine (SVM). The results show that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model, which is 78.27% and 0.042, but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best model for making predictions to determine the level of depression by using social media posts.