With COVID-19 paired with The Election, social media has been filing with grievances and opinions. However, this is nothing new for social media as this is a normal occurrence just at a higher volume than your everyday Twitter fight. For human behaviorists, psychologists, and organizations like; Microsoft Research and Georgia Tech in partnership with R. De Choudhury, CLPsych Conference, and the World Well Being Project, stressful events causing higher volumes of social media interactions create the opportunity to collect copious amounts of evidence. The World Health Organization had a goal regarding the “Comprehensive Mental Health Action Plan"( 2013–2020). This plan was to strengthen information systems for mental health, including increasing the capacity for population health monitoring by using social media and the development of computational infrastructure, so that mental health researchers could begin the efficient processing of “big data” mining. The idea for “Big data” mining through public health applications available via social media had been in an increasingly growing area of research and is referred to scientifically as “digital epidemiology, infoveiliance, and digital disease detection. For platforms especially like Twitter, due to their public Application Programming Interface, it has helped researchers view public opinions on vaccinations, public attitudes toward new tobacco products, and investigating various forms of drug abuse.
Today Dr. De Choudhury with her colleagues at Microsoft and Georgia Tech has been writing a series of research papers applying computational methods to the investigation regarding mental health on social media (Facebook, Twitter, and Reddit) by using crowd-sourced data set of tweets from Twitter users with “depression-indicative CES-D (Center for Epidemiological Studies-Depression)” scores. Data-sets can then used to train a statistical Machine Learning (ML) algorithm capable of identifying depression-indicative tweets witch gave results correlating well with US Centers for Disease Control depression data. Dr. De Choudhury had also done this for new mothers using public Twitter data using cue phrases (e.g. ‘it’s a boy/girl!’), then by analyzing characteristics of the new mothers’ Twitter stream before and after birth, with evidence suggesting that using ML techniques in conjunction with an analysis of pre-birth behavior patterns can predict postnatal emotional and behavioral changes with 71% accuracy. The CLPsych (Computational Linguistics and Clinical Psychology ) workshop series has provided an important forum for computer science clinical psychology researchers, focused on population mental health and social media. participants in the workshop were introduced to a method for developing data sets for specific mental illnesses: pulling tweets (via the public Twitter Application Programming Interface) from users who posted a publicly-stated psychiatric diagnosis, For example ‘I was diagnosed with having P.T.S.D’. By studying that individuals post, they began to analyze the linguistic characteristics of the person in consideration of their diagnosis.
As social media has become a part of a human’s everyday routine, it may open another route for honest firsthand observations on mental health. However, there are ethical issues that need to be considered before accepting the growing research field such as research on humans considering privacy and identity standardization, as well as computational standards for accuracy and objectivity concerning behavioral studies.
Mike Conway, Daniel O’Connor, Social media, big data, and mental health: current advances and ethical implications, Current Opinion in Psychology, Volume 9,2016, Pages 77-82, ISSN 2352-250X, https://doi.org/10.1016/j.copsyc.2016.01.004.
Alize J. Ferrari, Rosana E. Norman, Greg Freedman, Amanda J. Baxter, Jane E. Pirkis, Meredith G. Harris, Andrew Page, Emily Carnahan, Louisa Degenhardt, Theo Vos, Harvey A. Whiteford, The Burden Attributable to Mental and Substance Use Disorders as Risk Factors for Suicide: Findings from the Global Burden of Disease Study 2010 Published: April 2, 2014, https://doi.org/10.1371/journal.pone.0091936
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