Data and code for "Natural language processing reveals vulnerable mental health groups and heightened health anxiety on Reddit during COVID-19"

1. Data

Available at Open Science Framework: https://osf.io/7peyq/

Also available through Zenodo: https://zenodo.org/record/3941387#.YFfi3EhJHL8

Please cite if you use the data:

Low, D. M., Rumker, L., Talker, T., Torous, J., Cecchi, G., & Ghosh, S. S. Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit during COVID-19: An Observational Study. Journal of medical Internet research. doi: 10.2196/22635

@article{low2020natural,
  title={Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study},
  author={Low, Daniel M and Rumker, Laurie and Talkar, Tanya and Torous, John and Cecchi, Guillermo and Ghosh, Satrajit S},
  journal={Journal of medical Internet research},
  volume={22},
  number={10},
  pages={e22635},
  year={2020},
  publisher={JMIR Publications Inc., Toronto, Canada}
}

License: This dataset is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/ It was downloaded using pushshift API. Re-use of this data is subject to Reddit API terms.

1.1. Reddit mental health dataset

find in data/input/reddit_mental_health_dataset/

Posts and text features for the following timeframes from 28 mental health and non-mental health subreddits:

• 15 specific mental health support groups (r/EDAnonymous, r/addiction, r/alcoholism, r/adhd, r/anxiety, r/autism, r/bipolarreddit, r/bpd, r/depression, r/healthanxiety, r/lonely, r/ptsd, r/schizophrenia, r/socialanxiety, and r/suicidewatch)
• 2 broad mental health subreddits (r/mentalhealth, r/COVID19_support)
• 11 non-mental health subreddits (r/conspiracy, r/divorce, r/fitness, r/guns, r/jokes, r/legaladvice, r/meditation, r/parenting, r/personalfinance, r/relationships, r/teaching).

Downloaded using pushshift API. Re-use of this data is subject to Reddit API terms. Cite TODO if using this dataset.

filenames and corresponding timeframes:

• post: Jan 1 to April 20, 2020 (called "mid-pandemic" in manuscript; r/COVID19_support appears)
• pre: Dec 2018 to Dec 2019. A full year which provides more data for a baseline of Reddit posts
• 2019: Jan 1 to April 20, 2019 (r/EDAnonymous appears). A control for seasonal fluctuations to match post data.
• 2018: Jan 1 to April 20, 2018. A control for seasonal fluctuations to match post data.

See Supplementary Materials for more information.

Note: if subsampling (e.g., to balance subreddits), we recommend bootstrapping analyses for unbiased results.

1.2. COVID-19 mention dataset (Figure 1)

find in data/input/covid19_counts/

Same posts as in post above for 15 mental health subreddits.

Counting these tokens: 'corona','virus','viral','covid', 'sars','influenza','pandemic', 'epidemic', 'quarantine','lockdown', 'distancing', 'national emergency', 'flatten', 'infect','ventilator', 'mask','symptomatic', 'epidemiolog', 'immun', 'incubation', 'transmission','vaccine'

• One column covid19_boolean: if one of these words appears at least once (Figure 1)
• One column covid19_total: total count of words
• One column covid19_weighed_words: total count of words normalized by the amount of words (n_words) in a post (Figure S3).

1.3. COVID-19 cases

Confirmed COVID-19 cases obtained from ourworldindata.org/covid-cases (source: European CDC).

2. Reproduce

All .ipynb can run on Google Colab (for which data should be on Google Drive; code to load data from Google Drive is available in scripts) or on Jupter Notebook.

To run the .py or .ipynb on Jupter Notebook, create a virtual environment and install the requirements.txt:

• conda create --name reddit --file requirements.txt
• conda activate reddit

2.1. Preprocessing

• reddit_data_extraction.ipynb download data
• reddit_feature_extraction.ipynb feature extraction for classification (TF-IDF was re-done separately on train set), trend analysis, and supervised dimensionality reduction.
• See below for preprocessing for topic modeling and unsupervised clustering

2.2. Analyses

Classification

• Clone catpro from https://github.com/danielmlow/catpro/ and change path in run.py sys.path.append('./../../catpro') accordingly
• config.py set paths, subreddits to run, and sample size
• N is the model (0=SGD L1, 1=SGD EN, 2=SVM, 3=ET, 4=XGB)
• Run remotely: run_v8_<N>.sh runs run.py on cluster running each binary classifier on different nodes through --job_array_task_id set to one of range(0,15)
• Run locally (set --job_array_task_id and --run_modelN accordingly):

python3 -i run.py --job_array_task_id=1 --run_modelN=0 --run_version_number=8 

• classification_results.py: figure 5-a, summarize results, extract important features, and visualize testing on COVID19_support (psychological profiler), run (change paths accordingly)

Trend Analysis

• reddit_descriptive.ipynb: figures 1 and 2

Unsupervised clustering

• Unsupervised_Clustering_Pipeline.ipynb: figures 3 and 5-c

Topic Modeling

• reddit_lda_pipeline.ipynb: figure 4 and 5-b

Supervised dimensionality reduction

• reddit_cluster.ipynb: figure 6
• reddit_cluster.py: UMAP on 50 random subsamples of 2019 (pre) data to determine sensor precision
  • run remotely: run_umap.sh
  • run locally (--job_array_task_id will run a single subsample):

  python3 reddit_cluster.py --job_array_task_id=0 --plot=True --pre_or_post='pre'