Maintainers: Maintainers have write access to the repository. They are responsible for reviewing pull requests, providing feedback and ensuring consistency.
Collecting social media data for analysis can be kind of a nuisance. This project aims to make that collection process as simple as possible, by making some common-sense assumptions about what most researchers need, and how they like to work with their data. Collect social sits on top of other python libraries such as facepy (facebook) and tweepy (twitter). Our purpose is to take care of low level details and provide a clean API for working across multiple platforms.
Our goal is to make it as easy as possible for researchers to get up and running with new collections. Our focus is on ease of use over maximum features. At every decision point we should carefully consider how a new feature will impact simplicity. A user should be able to use collect-social without prior knowledge of underlying libraries and APIs. Based on our experience of underlying API we will attempt to make the best decision that should work in average case but if you are looking for maximum control over your collection process, consider using underlying libraries directly.
- Our current to-do can be found here here
- Command line interface
- Eventador integration.
- Refactor facebook API to match twitter.
- Save data to sqlite.
- Flat file exports (JSON).
- Option to upload file output to designated s3 bucket.
These are platforms we will consider supporting in the future. These will not be built out until we are happy with our implementation of facebook/twitter. If you are familiar with any of these platforms and would like to put together a proof of concept in a separate repository we welcome input.
- Disqus
- voat.co
- 4chan/8chan etc.
If you are looking to get started contributing please see our contributor guide. TODO - write guide
Collect-social is built to run on python 3.6.
git clone https://github.com/Data4Democracy/collect-social.git
Then install as a package using pip. This will allow you import collect_social from any python script.
cd collect-social
pip install .Or you can use the setuptools directly with
python setup.py installTODO: Explain settings file
If you haven't already, make sure to create a Facebook app with your Facebook developer account. This will give you an app id and app secret that you'll use to query Facebook's graph API.
Note that you'll only be able to retrieve content from public pages that allow API access.
Caution work in progress. This will change as part of our ongoing refactor. We do not suggest anyone use this for now.
You can retrieve posts using Facebook page ids. Note that the page id isn't the same as page name in the URL. For example Justin Beiber's page name is JustinBieber, but the page id is 67253243887. You can find a page's id by looking at the source HTML at doing a ctrl+f (find in page) for pageid. Here's a longer explanation.
from collect_social.facebook import get_posts
app_id = '<YOUR APP ID>'
app_secret = '<YOUR APP SECRET>'
connection_string = 'sqlite:////full/path/to/a/database-file.sqlite'
page_ids = ['<page id 1>','<page id 2>']
get_posts.run(app_id,app_secret,connection_string,page_ids)This will run until it has collected all of the posts from each of the pages in your page_ids list. It will create post, page, and user tables in the sqlite database from the file passed in connection_string.
The database will be created if it does not already exist.
Note: The app_id, app_secret and elements in the page_ids list are all strings, and should be quoted (' ' or " ").
If you like, quickly check the success of your program by viewing the first 10 posts:
sqlite3 database-file.sqlite "SELECT message FROM post LIMIT 10"Caution work in progress. This will change as part of our ongoing refactor. We do not suggest anyone use this for now.
This will retrieve all the comments (including threaded replies) for a list of posts. You can optionally provide a max_comments value, which is helpful if you're grabbing comments from the Facebook page of a public figure, where posts often get tens of thousands of comments.
from collect_social.facebook import get_comments
app_id = '<YOUR APP ID>'
app_secret = '<YOUR APP SECRET>'
connection_string = 'sqlite:////full/path/to/a/database-file.sqlite'
post_ids = ['<post id 1>','<post id 2>']
get_comments.run(app_id,app_secret,connection_string,post_ids,max_comments=5000)This will create post, comment, and user tables in the sqlite database created in/opened from the file passed in connection_string, assuming those tables don't already exist.
Caution work in progress. This will change as part of our ongoing refactor. We do not suggest anyone use this for now.
Reactions are "likes" and all the other happy/sad/angry/whatever responses that you can add to a Facebook post without actually typing a comment. The reaction author_id and reaction_type are saved to an interaction table in your sqlite database.
from collect_social.facebook import get_reactions
app_id = '<YOUR APP ID>'
app_secret = '<YOUR APP SECRET>'
connection_string = 'sqlite:////full/path/to/a/database-file.sqlite'
post_ids = ['<post id 1>','<post id 2>']
get_reactions.run(app_id,app_secret,connection_string,post_ids,max_comments=5000)If you haven't already, make sure to create a Twitter app with your Twitter account. This will give you an access token, access token secret, consumer key, and consumer secret that will be required to query the Twitter API.
This assumes you have a list of Twitter accounts you'd like to use as seeds. This will build a network of those seeds and the accounts the seeds follow, and collect the last 3,200 tweets (the limit allowed by Twitter's API) for each of those accounts.
from collect_social.twitter.utils import setup_db, setup_seeds
from collect_social.twitter.get_profiles import run as run_profiles
from collect_social.twitter.get_friends import run as run_friends
from collect_social.twitter.get_tweets import run as run_tweets
# These you generate on developers.twitter.com
consumer_key = 'YOUR KEY'
consumer_secret = 'YOUR SECRET'
access_key = 'YOUR ACCESS KEY'
access_secret = 'YOUR ACCESS SECRET'
# Path to your sqlite file
connection_string = 'sqlite:///db.sqlite'
args = [consumer_key, consumer_secret, access_key, access_secret, connection_string]
# Assuming your seed accounts are in a file called `seeds.txt`. Put each screen name
# on its own line in the file
seeds = [l.strip() for l in open('seeds.txt').readlines()]
db = setup_db(connection_string)
setup_seeds(db, consumer_key, consumer_secret, access_key, access_secret, screen_names=seeds)
# get user profiles
run_profiles(*args)
# get everyone they follow
run_friends(*args)
# get profiles for newly added users
run_profiles(*args)
# get everyone's last 3200 tweets
run_tweets(*args)You now have a sqlite database with user, tweet, mention, url, hashtag, and connection tables, that all can be pulled into a pandas.DataFrame.
import pandas as pd
import sqlite3
con = sqlite3.connect("db.sqlite")
df_tweet = pd.read_sql_query("SELECT * from tweet", con)
df_user = pd.read_sql_query("SELECT * from user", con)