Social media big data analysis for mental health research

2022 ◽  
pp. 109-143
Author(s):  
Akkapon Wongkoblap ◽  
Miguel A. Vadillo ◽  
Vasa Curcin
2021 ◽  
Author(s):  
Selina Gong ◽  
John Morris ◽  
Yu Sun

Today’s students are faced with stress and anxiety as a result of school or work life and have added pressure from social media and technology. Stress is heavily related to many symptoms of depression such as irritability or difficulty with concentration as well as symptoms of anxiety like restlessness or feeling tired. Some of these students are able to find a healthy outlet for stress, however other students may not be able to. We have created a program where students will be able to destress and explore their emotions with the help of suggestions from our system based on previously explored thoughts. Our program uses machine learning to help students get the most effective stress relief by suggesting different mental health exercises to try based on input given by the user and provides emotional comfort based on the user’s preferences.


Author(s):  
Frances Shaw

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.


2017 ◽  
Vol 41 (3) ◽  
pp. 129-132 ◽  
Author(s):  
Peter Schofield

SummaryAdvances in information technology and data storage, so-called ‘big data’, have the potential to dramatically change the way we do research. We are presented with the possibility of whole-population data, collected over multiple time points and including detailed demographic information usually only available in expensive and labour-intensive surveys, but at a fraction of the cost and effort. Typically, accounts highlight the sheer volume of data available in terms of terabytes (1012) and petabytes (1015) of data while charting the exponential growth in computing power we can use to make sense of this. Presented with resources of such dizzying magnitude it is easy to lose sight of the potential limitations when the amount of data itself appears unlimited. In this short account I look at some recent advances in electronic health data that are relevant for mental health research while highlighting some of the potential pitfalls.


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