Depression Detection using Machine and Deep Learning Models to Assess Mental Health of Social Media Users

Abstract

During the COVID-19 pandemic millions of people were affected due to quarantine and restrictions. With more than half of the world's population active on social media, people resorted to these platforms as their outlet for emotions. This led to researchers analysing content on social media to detect depression by studying the patterns of content posting. This paper focuses on finding a data-driven metric called ‘Happiness Factor’ of a user to assess their mental health. Various models were trained to classify a post as ‘depressed’. A user’s ‘Happiness Factor’ was calculated based on the nature of their posts. This metric identifies degrees of depression of a user. The results show the effectiveness of the classifier in identifying the depression level. Also, a Mental Health Awareness Resource System is proposed which recommends mental health awareness resources to users on their social media interface based on their ‘Happiness Factor’.