Code base for my work on predicting health changes via changes in social media behavior.
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README.md
bgfunc.R
bgfunc.py
collect.py
cron.py
date_checker.py
eda-instagram-bayes.R
eda-instagram.ipynb
eda-twitter-bayes.R
eda-twitter.ipynb
extract.py
face-detection.ipynb
get_timezones.py
mixture_models.ipynb
photo_ratings_queries.json
run.py
twbgfunc.R
util.py
varset.R
verify.py

README.md

Data Collection
run.py handles all of the RESTful/outward-facing requests (eg. /verify/<username>).

collect.py and verify.py have medium-specific methods for verifying that a participant has followed us, and collecting their data.

  • /collect is run either manually or as a cron job every 15 minutes (when turned on)
  • it looks for any usernames with collected=0 in the usernames table
extract.py feeds subroutines to the /collect regime for feature extraction.

util.py has various database and other helper functions.

Analysis

  • eda-instagram and eda-twitter are the analytical frames except for Bayesian stuff
  • *-bayes.R for Bayesian logistic regression and convergence checks
  • bgfunc contains all the heavy lifting for analysis and processing
  • face-detection compiles, trains, and verifies face detection algo
  • mixture-models has code for Kalman filter, GMM, and HMM