EmoCure: Utilising Social Media Data and Smartphones to Predict and Cure Depression

Abstract

Depression is a common and recurring illness. It is a serious medical condition which might lead to self-harm. Various factors like unawareness about depression, shortage of medical health professionals and social stigmas make proper treatment for depression inaccessible. With the growing importance of technology in every sector of life, healthcare systems have also started using technology to provide better treatment. Various studies have shown that the widespread use of smartphones can be useful in predicting as well as treating depression by recommending activities. Smartphones can be very useful in the continuous monitoring of a patient which in turn helps to keep track of the activities of the patient. Social media data can also be used to find out the mental state of a patient. We propose EmoCure, a smartphone application that uses social media data, wearable sensor data, patient history, and smartphone usage patterns to predict, monitor, and treat depression using emotion regulating activities. We use machine learning models for finding out the sentiments in the social media posts. To predict depression, we use ensemble learning. We then recommend personalized emotion regulating activities whichever the user prefers.