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Code for fitting a topic and point of view (POV) model of collective intelligence processes, using parallel Gibbs sampling.

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topic-pov

Code for fitting a topic and point of view (POV) model of collective intelligence processes, using parallel Gibbs sampling. Also includes synthetic data generation for testing and evaluation.

To compile and run:

make
bash test_synth.sh 50 500

You will need the GNU Scientific Library (GSL) headers installed to compile everything. See comments in test_synth.sh for a high-level overview of the inference process.

Files associated with executables (descriptions at the top of each file):

  • basicstats.c
  • check_indexes.c
  • compare_users.c
  • inference.c
  • initialize.c
  • page_user_stats.c
  • readout.c
  • set_assignments.c
  • store_revisions.c
  • verify_mmaps.c
  • word_probability.c

Shared functions, struct definitions (descriptions in header files):

  • index.h
  • comparisons.h / comparisons.c
  • parse_mmaps.h / parse_mmaps.c
  • probability.h / probability.c
  • sample.h / sample.c

Python helper scripts (in bin/):

  • compare_clusterings.py
  • synth.py
  • topic_pov_to_clusters.py

Bash to glue everything together:

  • test_synth.sh
  • initialize.sh

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Code for fitting a topic and point of view (POV) model of collective intelligence processes, using parallel Gibbs sampling.

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