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CrowdEval

CrowdEval is an experimental crowdsourced factchecking application, created as part of my MComp Computer Science dissertation project. The idea for this project was provided by the project supervisor, Carolina Scarton.

Test & Lint Code style: black js-standard-style

CrowdEval ships with two Docker environments:

  • a development environment with containerised infrastructure, but the app and frontend run natively
  • a fully containerised production environment

Installation of Development Environment

Dependencies:

  1. cd into the webapp directory
  2. Run poetry install
    • On macOS Big Sur, if Numpy fails to install, ensure the environment variable SYSTEM_VERSION_COMPAT is set to 1 and try again
  3. cp .env.dev .env
    • Enter a SECRET_KEY, which can be any string
    • Enter the TWITTER_ API keys. This has to be a new-style Twitter project because we use v2.0 of the Twitter API
      • The callback URL for development should be localhost:5000/login/twitter/authorized
    • Enter the RECAPTCHA_ API keys
    • The remainder of the file is already configured for the development environment and shouldn't need to be changed
  4. Run docker-compose up to start
  5. Once the Elasticsearch service has started, run: 
    $ poetry run flask create-index -i posts -c infrastructure/elasticsearch/posts.json
    This will create the required posts index.
  6. Install frontend dependencies with npm install
  7. To start asset compilation and the Flask dev server, run 
    $ npm run start
    The app will be started on localhost:5000
  8. Migrate the database with poetry run flask db upgrade

Deploying to production

Working from the root directory:

  1. cp webapp/.env.prod webapp/.env
    • Enter a SECRET_KEY, which should be a random ~32-character secret
    • Enter the TWITTER_ API keys. This has to be a new-style Twitter project because we use v2.0 of the Twitter API
      • The callback URL should be <your hostname>/login/twitter/authorized
    • Enter the RECAPTCHA_ API keys
    • The remainder of the file is already configured for the production environment and shouldn't need to be changed
  2. docker-compose up

The application should now be built and started via Gunicorn.

Administrative commands

In development, these commands should be prefixed with poetry run (or run poetry shell once to activate and run as is).

In production, attach to the crowdeval service i.e.:

docker-compose run --entrypoint "bash -l" crowdeval

and then run the commands, although note that flask needs to be run from ./venv/bin/flask to ensure the correct version is used.

Seeding with dummy data

The system can import the Kochkina et al.'s PHEME dataset, which has been pre-processed and stored in /seeds/kochkina_et_al_PHEME. To import it:

$ flask import-tweet-seeds seeds/kochkina_et_al_PHEME

This will create a .veracities.json file, which can then be used to seed random (but biased towards the dataset's veracity) ratings with

$ flask seed-ratings

Regenerating explore cache

The explore by rating pages are served from Redis, and must be manually regenerated.

$ flask recache-explore

In production this is probably best run as a scheduled task on the host machine via cron with an entry such as (note the hard coded path to the docker-compose.yml file):

*/5 * * * * /usr/local/bin/docker-compose -f /data/crowdeval/docker-compose.yml run --entrypoint "venv/bin/flask recache-explore" crowdeval >> crowdeval-cron.log 2>&1

Quality Control

Tests and linting can be run with

$ flask test
$ flask lint