Skip to main content

Archive Gab.ai posts from the command line

Project description

garc

Garc is a python library and command line tool for collecting JSON data from Gab.ai

Garc is built based on the wonderful twarc project published by the Documenting the Now project. Inspiration for structure, usage and outputs are from twarc, and garc is intended to be used for similar purposes.

Garc is still very much a work in progress, and is being constantly updated to add deeper functionality and as new features and changes are implemented by Gab.

Warnings

Gab's api is relatively sparsely documented, so things may change without warning and break searches.

Please be respectful when using this and any data collection software, try not to make excessive searches and calls.

Installation

There are two options for installing garc.

  1. From pypi the official python package repository, which will always have the most stable release: pip install garc
  2. Directly from Github, which will have the newest release: pip install git+git://github.com/ChrisStevens/garc.git

Usage

Configure

First you need to give garc your account information:

garc configure

You only need the username and password for your account created at Gab.ai. Without an account you won't be able to interact with the api, or get any results from garc.

Search

Using the Gab search API you can collect posts based on a search term. As Gab's API is mostly undoumented it is hard to know exactly what the searches return (it is however the same data as appears on the Gab website for a search). Initial tests have found matches for the search term in both the post body and the users description, and the search uses some type of fuzzy matching or word stemming, as matches for not exact terms have shown up.

A simple call

garc search maga

Will return as many historical gabs as are available (usually around 9000 irrespective of post dates).

You can also limit the number of returns with the --number_gabs parameter

garc search maga --number_gabs=100

Which will return approxiately 100 of the most recent posts.

User Posts

Another way to collect posts is by collecting all the posts made by a single user

garc userposts fakeusername

As some users have a large number of posts this can take a long time to collect the entirey of a users timeline. Additional there are both number and time filters you can pass to limit the number of posts.

garc userposts fakeusername --number_gabs=100

Will return aproximately 100 posts from the top of a users timeline

garc userposts fakeusername --gabs_after=2018-05-12

Will return all gabs from after 2018-05-12

User info

You can also collect the information of a user

garc user fakeusername

Which will return a json object of information about the user

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

garc-1.1.5.tar.gz (6.4 kB view details)

Uploaded Source

Built Distribution

garc-1.1.5-py2.7.egg (13.6 kB view details)

Uploaded Source

File details

Details for the file garc-1.1.5.tar.gz.

File metadata

  • Download URL: garc-1.1.5.tar.gz
  • Upload date:
  • Size: 6.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.19.1 setuptools/40.0.0 requests-toolbelt/0.8.0 tqdm/4.24.0 CPython/2.7.6

File hashes

Hashes for garc-1.1.5.tar.gz
Algorithm Hash digest
SHA256 fc110d83deed698e05c158e4570511089a2d094b90674750defc64d68ef36929
MD5 d601070069233e745e81cc14445046c9
BLAKE2b-256 0a8981a09955546d87b26e2809a1668312d1fdd4bcd3b326b3752ad070d88322

See more details on using hashes here.

File details

Details for the file garc-1.1.5-py2.7.egg.

File metadata

  • Download URL: garc-1.1.5-py2.7.egg
  • Upload date:
  • Size: 13.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.11.0 pkginfo/1.4.2 requests/2.18.4 setuptools/40.0.0 requests-toolbelt/0.8.0 tqdm/4.24.0 CPython/2.7.6

File hashes

Hashes for garc-1.1.5-py2.7.egg
Algorithm Hash digest
SHA256 de7709da35a5d98f069a3ac30a6c5096c434d28bfc88340b19687bdc0779dc3d
MD5 8a66afd68feb1064dc0f101cc5171b90
BLAKE2b-256 09f80cce8571b4b265aa6bbd71836be2570a4ccf14475e0fc2fb40aa36f6eab4

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page