Investigating Patient Attitudes Towards the use of Social Media Data to Augment Depression Diagnosis and Treatment: a Qualitative Study

In this paper, we use qualitative research methods to investigate the attitudes of social media users towards the (opt-in) integration of social media data with routine mental health care and diagnosis. Our investigation was based on secondary analysis of a series of five focus groups with Twitter users, including three groups consisting of participants with a self-reported history of depression, and two groups consisting of participants without a self reported history of depression. Our results indicate that, overall, research participants were enthusiastic about the possibility of using social media (in conjunction with automated Natural Language Processing algorithms) for mood tracking under the supervision of a mental health practitioner. However, for at least some participants, there was skepticism related to how well social media represents the mental health of users, and hence its usefulness in the clinical context.

[1]  Son Doan,et al.  BioCaster: detecting public health rumors with a Web-based text mining system , 2008, Bioinform..

[2]  Annie T. Chen,et al.  What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques , 2015, Journal of medical Internet research.

[3]  Georgina Kennedy,et al.  Characterizing Twitter Discussions About HPV Vaccines Using Topic Modeling and Community Detection , 2016, Journal of medical Internet research.

[4]  Jessica Keune,et al.  Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer’s Disease Stigma on Twitter , 2017, The journals of gerontology. Series B, Psychological sciences and social sciences.

[5]  Mike Conway,et al.  Ethical issues in using Twitter for population-level depression monitoring: a qualitative study , 2016, BMC Medical Ethics.

[6]  Eric Horvitz,et al.  Characterizing and predicting postpartum depression from shared facebook data , 2014, CSCW.

[7]  Gregory J. Park,et al.  Automatic personality assessment through social media language. , 2015, Journal of personality and social psychology.

[8]  Jeffery L. Painter,et al.  Social Media Listening for Routine Post-Marketing Safety Surveillance , 2016, Drug Safety.

[9]  W. Chapman,et al.  Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products , 2013, Journal of medical Internet research.

[10]  Michael D. Barnes,et al.  Tracking suicide risk factors through Twitter in the US. , 2014, Crisis.

[11]  Mark Dredze,et al.  Quantifying Mental Health Signals in Twitter , 2014, CLPsych@ACL.

[12]  Olga V. Demler,et al.  The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). , 2003, JAMA.

[13]  Michael D. Barnes,et al.  Tweaking and Tweeting: Exploring Twitter for Nonmedical Use of a Psychostimulant Drug (Adderall) Among College Students , 2013, Journal of medical Internet research.

[14]  Maarten Sap,et al.  Towards Assessing Changes in Degree of Depression through Facebook , 2014, CLPsych@ACL.

[15]  Kenneth D. Mandl,et al.  HealthMap: Global Infectious Disease Monitoring through Automated Classification and Visualization of Internet Media Reports , 2008, Journal of the American Medical Informatics Association.

[16]  J. Kitzinger,et al.  Qualitative Research: Introducing focus groups , 1995 .

[17]  Leonardo Max Batista Claudino,et al.  Beyond LDA: Exploring Supervised Topic Modeling for Depression-Related Language in Twitter , 2015, CLPsych@HLT-NAACL.

[18]  Kevin A Padrez,et al.  Linking social media and medical record data: a study of adults presenting to an academic, urban emergency department , 2015, BMJ Quality & Safety.

[19]  H. Boeije A Purposeful Approach to the Constant Comparative Method in the Analysis of Qualitative Interviews , 2002 .

[20]  E. Goffman The Presentation of Self in Everyday Life , 1959 .

[21]  Mike Conway,et al.  Social Media, Big Data, and Mental Health: Current Advances and Ethical Implications. , 2016, Current opinion in psychology.

[22]  Glen Coppersmith,et al.  Quantifying the Language of Schizophrenia in Social Media , 2015, CLPsych@HLT-NAACL.

[23]  Margaret Volante Qualitative research. , 2008, Nurse researcher.

[24]  Christoph Lofi,et al.  Crowdsourcing Twitter annotations to identify first-hand experiences of prescription drug use , 2015, J. Biomed. Informatics.

[25]  Maarten Sap,et al.  The role of personality, age, and gender in tweeting about mental illness , 2015, CLPsych@HLT-NAACL.

[26]  Alberto Maria Segre,et al.  The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic , 2011, PloS one.