Ensemble Neural Models for Depressive Tendency Prediction Based on Social Media Activity of Twitter Users

Abstract

In view of the ongoing pandemic, Clinical Depression (CD) is a serious health challenge for a large segment of the population. According to recent public surveys, more than 30 million American citizens are the victim of depression each year and depression also causes 30 thousand suicides each year. Early detection of depression can help provide much needed medical intervention and treatment for better mental health. Toward this, the social media posts of users can be a significant source for analyzing their mental health signals, and can also serve as a measure for assessing the prevalence of clinical depression tendencies in the population. In this paper, an approach that leverages the predictive power of supervised and semi-supervised learning algorithms for detecting depressive tendencies in the population using social media activity is presented. Learning models were trained on preprocessed tweet data from the Sentiment140 dataset containing 1.6 million labeled tweets. We also designed a convolution neural network model for the prediction task that outperformed machine learning models by a significant margin with an accuracy of 97.1%. The performance of the proposed models is benchmarked using standard metrics like SMDI (Social Media Depression Index). Crowd-sourcing approaches were adopted for collecting real-time social behavior of users to train the proposed model and demonstrate its potential for real-world applications. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.