Code accompanying the paper "Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis" (CLPsych workshop @NAACL 2021)
Purpose: Identify Reddit users with a self-reported bipolar disorder diagnosis and profile their user characteristics
Citation
To refer to this work, please cite the following paper:
@inproceedings{
author = {Jagfeld, Glorianna and Lobban, Fiona and Rayson, Paul and Jones, Steven H.},
booktitle = {Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
at NAACL 2021},
title = {{Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis}},
year = {2021}
}
Visual summary of the results
Requirements
python 3.8, nltk 3.5, pandas 1.2.3
Folder structure
The data/ folder contains examples (paraphrased from real posts) for submissions and comments. To receive the full original dataset under a Data Usage Agreement, please contact .
The output/ folder will contain any output files generated by the three steps below
The disclosure-patterns/ folder contains self-reported diagnosis patterns and mental health diagnosis terms (see separate README.txt within the folder)
Code
- Identify self-reported diagnosis posts
-
Build patterns: For each diagnosis (CONDITION), CONDITION_inclusion-patterns.txt and CONDITION_exclusion-patterns.txt are saved to /disclosure-patterns/expanded-diagnosis-patterns/
-
Apply patterns: Prints post.id \t post.user_name to one file for each diagnosis and input file to the output/ folder
- Extract self-reported age and gender Extracts self-reported age and gender from submission titles, writes results to output/users_age_gender.csv
Run all steps on example data (demonstrates code usage): python run_example.py