Click on one of the icons, or navigate to a social media home page.
Select all text (Cmd + A), then navigate back to the clinic.
Paste the text in the text box below, then click the "Submit" button below.
What It Is, And How It Works
This project simulates a mental health diagnostic survey that takes user data and produces a profile with potential diagnoses. The code for this project simulates the extraction of user data through patient-input information (copying and pasting or typing), runs the text through an external text sentiment analysis API to retrieve the positive/negative connotation of the text, then extracts a random diagnosis from one of three sets of fake diagnoses.
Purpose
While this project is partially intended to reveal the potential benefits of using user data for diagnoses, the random diagnosis is supposed to create confusion for any user that gets a slightly inaccurate diagnosis.
Background
While many studies have been done to correlate different kinds of media exposure to different behavior and mental health issues (i.e. violent video games have been correlated with higher levels of aggression in children, social media use has been correlated with lower self-esteem in females), very doctors I know have asked about my online behavior, and few online behavioral and mental health diagnostic surveys acknowledge the correlations through their questions. I believe that one of the main reasons it is hard to discuss internet behavior between patients and primary care clinicians is due to the large range of activities that can be done online that havent been studied, categorized, or correlated with mental health issues. When we began studying issues with user data tracking in class, I started to think about how user data could potentially be used to benefit users rather than exploit them if it is used to contextualize certain diagnoses.