Studying social anxiety without triggering it: Establishing an age-controlled cohort of social media users for observational studies
Schmidt, A. L.; Gonzalez-Hernandez, G.; O'COnnor, K.; Rodriguez-Esteban, R.
Show abstract
BackgroundPatients of certain diseases are less likely to approach the healthcare system but remain active in social media. Young Social Anxiety Disorder (SAD) patients, in particular, are a hard-to-reach population due to disease symptomatology, unmet need and age-related barriers, which makes obtaining first-hand access to patient perspectives challenging. ObjectiveTo create a curated cohort of patients from social media that report their age in the range of 13 to 25 years old and confirm having a SAD diagnosis or having received therapy for SAD, and to assess the value of the content posted by these users for observational studies of SAD. MethodsWe collected 535k posts by 118k Reddit users from the r/SocialAnxiety subreddit. We then developed precise regular expressions to extract age, diagnosis and therapy mentions. We manually annotated the full set of expressions extracted and double-annotated 5% of the age mentions and 10% of the diagnosis and therapy mentions. Using similar methodology, we identified mentions of comorbidities and substance use. ResultsOur validated cohort includes 37,073 posts by 1,102 users that meet the inclusion criteria. The age, diagnosis, and therapy mention detection had a precision of 68%, 31%, and 44%, respectively, with an inter-annotator agreement of 0.96, 0.96, and 0.78. Sixty-one percent of the users in the cohort report having one or more comorbidities on top of their SAD diagnosis (Fleisss Kappa=0.79) and 13% report a concerning use of drugs or alcohol (Fleisss Kappa=0.87). We compared the characteristics of our social media cohort to the published literature on SAD. ConclusionsPatients with SAD post actively on Reddit and their perspectives can be captured and studied directly from these data. Extracting age, therapy, substance abuse and comorbidities (and potentially other patient data) can address realworld data source biases. Thus, social media is a valuable source to create cohorts of hard-to-reach patient populations that may not enter the healthcare system.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Patients with affective disorders profit most from telemedical treatment: Evidence from a naturalistic patient cohort during the COVID-19 pandemic 94%
- Use of the Internet and digital devices among people with severe mental ill health during the COVID-19 pandemic restrictions 93%
- Understanding Psychiatric Illness Through Natural Language Processing (UNDERPIN): Rationale, Design, and Methodology 93%
Similar papers in this journal
- Validation of Visual and Auditory Digital Markers of Suicidality in Acutely Suicidal Psychiatric In-Patients 93%
- Uncovering social states in healthy and clinical populations using digital phenotyping and Hidden Markov Models 93%
- Social networking service, patient-generated health data, and population health informatics: patterns and implications for using digital technologies to support mental health 92%
Similar papers in this journal
- Development of the NeuroFlow Severity Score and Comparison With Validated Measures for Depression and Anxiety 92%
- Development and use analysis of ‘gestioemocional.cat’, a web app for promoting emotional self-care and access to professional mental health resources during the covid-19 pandemic 92%
- Evaluating the Clinical Feasibility of an Artificial Intelligence-Powered Clinical Decision Support System: A Longitudinal Feasibility Study 92%
Similar papers in this journal
- The influence of repeated mild lockdown on mental and physical health during the COVID-19 pandemic: a large-scale longitudinal study in Japan 92%
- Improving ascertainment of suicidal ideation and suicide attempt with natural language processing 92%
- Is Kambo psychoactive? Acute and subacute effects of the secretion of the Giant Maki Frog (Phyllomedusa bicolor) on human consciousness. 92%
Similar papers in this journal
- Post-infection depressive, anxiety and post-traumatic stress symptoms: a retrospective cohort study with mild COVID-19 patients 92%
- Neurocognition and its association with adverse childhood experiences and familial risk of mental illness 90%
- A computational and multi-brain signature for aberrant social coordination in schizophrenia 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.