Predicting Depression via Social Media
Reviewer: Nishant Yadav(11010147)
Problem Statement:
People generally use social media like facebook, twitter on a daily basis to post about their emotions and their life. So, it is very good platform for studying mental health of public. Thus, the problem statement being detecting and predicting Major Depressive Disorder (MDD) in individuals using micro blog posts on twitter. The problem is clearly defined in the paper.
Importance of the problem:
Mental issues are very common in the today’s world and many times individuals suffering from mental depression do not get adequate treatment. Mental depression also leads to severe health related issues. WHO in 2001 estimated that nearly 300 million people in the world suffer from depression and also many countries do not have adequate facilities available for detecting and treating
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Recently, researchers have tried to predict flu and epidemics using social media. Some research has also been done to predict depression using web activity, facebook and twitter but in the cuurent paper authors expand the measures for predicting depression from social media. They also show that their measures can be used to build predictors to predict the depression well before its onset.
Approach for solving problem:
The authors have first used crowdsourcing to obtain the twitter users who have been diagnosed for MDD using CES-D2 (Center for Epidemiologic Studies Depression Scale) screening test. They then propose some measures and analyse their twitter activity from one year before the onset of MDD to study their behaviour on those measures. The measures include: social engagement and emotions, linguistic styles etc. They then compare these people’s twitter activity and standard users based on these measures to build a MDD classifier to predict MDD before its onset in individuals.