Predicting Depression Levels using Social Media Posts
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Predicting Depression Levels using Social Media Posts
Table of Contents
Problem Statement
Research Objective
Assumptions of the Study
Research Questions
Literature Review
Research Methods
Research Findings
Reflective Summary
From the profile of social media users, it is possible to gather all the information relating to their mood and negativism. The paper seeks to investigate how the users? social media posts can be used to group users based on their mental health levels. The detection of a mental disorder in its early stages is a challenging process given that it requires clinical interventions that are less feasible in most instances. Social media have portrayed promising characteristics that are instrumental in detecting and characterizing mental disorders as early as possible. In this study, the use of user generated contents can be used to predict depression as well as its level of severity. In addition, the use of user generated contents can help to understand the difference between the linguistic abilities of the users. The study involves the use of linguistic analysis models to identify depressive posts from less depressive ones. The degree of a depressive post is identified through the use of valence values on the basis of mood tag. In this regard, the paper seeks to extensively scrutinize the effectiveness of depending on social media posts to predict the degree of depression of the users.

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