Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/freddyheppell/crowdeval
A crowdsourced system for multiplatform rumour evaluation
https://github.com/freddyheppell/crowdeval
bert flask misinformation nlp python rumour-evaluation university-of-sheffield
Last synced: 1 day ago
JSON representation
A crowdsourced system for multiplatform rumour evaluation
- Host: GitHub
- URL: https://github.com/freddyheppell/crowdeval
- Owner: freddyheppell
- License: gpl-3.0
- Created: 2021-02-12T14:36:20.000Z (almost 4 years ago)
- Default Branch: dev
- Last Pushed: 2022-08-21T09:06:43.000Z (over 2 years ago)
- Last Synced: 2025-01-11T15:10:36.152Z (9 days ago)
- Topics: bert, flask, misinformation, nlp, python, rumour-evaluation, university-of-sheffield
- Language: Python
- Homepage:
- Size: 3.24 MB
- Stars: 0
- Watchers: 4
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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](https://carolscarton.github.io/).
[](https://github.com/freddyheppell/crowdeval/actions/workflows/test.yml) [](https://github.com/psf/black) [](http://standardjs.com)
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:
* Python 3.9.4
* [Poetry](https://python-poetry.org/)
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](https://developer.twitter.com/en/docs/projects/overview) 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](https://developer.twitter.com/en/docs/projects/overview) because we use v2.0 of the Twitter API
* The callback URL should be `/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.:
> ```shell
> 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](/seeds/kochkina_et_al_PHEME). To import it:
```shell
$ 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
```shell
$ flask seed-ratings
```
### Regenerating explore cache
The explore by rating pages are served from Redis, and must be manually regenerated.
```shell
$ 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
```shell
$ flask test
$ flask lint
```