Translations: pt-BR
twarc is a command line tool and Python library for archiving Twitter JSON data. Each tweet is represented as a JSON object that is exactly what was returned from the Twitter API. Tweets are stored as line-oriented JSON. Twarc will handle Twitter API's rate limits for you. In addition to letting you collect tweets Twarc can also help you collect users, trends and hydrate tweet ids.
twarc was developed as part of the Documenting the Now project which was funded by the Mellon Foundation.
Before using twarc you will need to register an application at apps.twitter.com. Once you've created your application, note down the consumer key, consumer secret and then click to generate an access token and access token secret. With these four variables in hand you are ready to start using twarc.
- install Python (2 or 3)
- pip install twarc
First you're going to need to tell twarc about your API keys:
twarc configure
Then try out a search:
twarc search blacklivesmatter > search.jsonl
Or maybe you'd like to collect tweets as they happen?
twarc filter blacklivesmatter > stream.jsonl
See below for the details about these commands and more.
Once you've got your application keys you can tell twarc what they are with the
configure command.
twarc configure
This will store your credentials in a file called .twarc in your home
directory so you don't have to keep entering them in. If you would rather supply
them directly you can set them in the environment (CONSUMER_KEY,
CONSUMER_SECRET, ACCESS_TOKEN, ACCESS_TOKEN_SECRET) or using command line
options (--consumer_key, --consumer_secret, --access_token,
--access_token_secret).
This uses Twitter's search/tweets to download pre-existing tweets matching a given query.
twarc search blacklivesmatter > tweets.jsonl
It's important to note that search will return tweets that are found within a
7 day window that Twitter's search API imposes. If this seems like a small
window, it is, but you may be interested in collecting tweets as they happen
using the filter and sample commands below.
The best way to get familiar with Twitter's search syntax is to experiment with Twitter's Advanced Search and copy and pasting the resulting query from the search box. For example here is a more complicated query that searches for tweets containing either the #blacklivesmatter or #blm hashtags that were sent to deray.
twarc search '#blacklivesmatter OR #blm to:deray' > tweets.jsonl
Twitter attempts to code the language of a tweet, and you can limit your search to a particular language if you want:
twarc search '#blacklivesmatter' --lang fr > tweets.jsonl
You can also search for tweets with a given location, for example tweets mentioning blacklivesmatter that are 1 mile from the center of Ferguson, Missouri:
twarc search blacklivesmatter --geocode 38.7442,-90.3054,1mi > tweets.jsonl
If a search query isn't supplied when using --geocode you will get all tweets
relevant for that location and radius:
twarc search --geocode 38.7442,-90.3054,1mi > tweets.jsonl
The filter command will use Twitter's statuses/filter API to collect tweets as they happen.
twarc filter blacklivesmatter,blm > tweets.jsonl
Please note that the syntax for the Twitter's track queries is slightly different than what queries in their search API. So please consult the documentation on how best to express the filter option you are using.
Use the follow command line argument if you would like to collect tweets from
a given user id as they happen. This includes retweets. For example this will
collect tweets and retweets from CNN:
twarc filter --follow 759251 > tweets.jsonl
You can also collect tweets using a bounding box. Note: the leading dash needs to be escaped in the bounding box or else it will be interpreted as a command line argument!
twarc filter --locations "\-74,40,-73,41" > tweets.jsonl
If you combine options they are OR'ed together. For example this will collect tweets that use the blacklivesmatter or blm hashtags and also tweets from user CNN:
twarc filter blacklivesmatter,blm --follow 759251 > tweets.jsonl
Use the sample command to listen to Twitter's statuses/sample API for a "random" sample of recent public statuses.
twarc sample > tweets.jsonl
The dehydrate command generates an id list from a file of tweets:
twarc dehydrate tweets.jsonl > tweet-ids.txt
Twarc's hydrate command will read a file of tweet identifiers and write out the tweet JSON for them using Twitter's status/lookup API.
twarc hydrate ids.txt > tweets.jsonl
Twitter API's Terms of Service discourage people from making large amounts of raw Twitter data available on the Web. The data can be used for research and archived for local use, but not shared with the world. Twitter does allow files of tweet identifiers to be shared, which can be useful when you would like to make a dataset of tweets available. You can then use Twitter's API to hydrate the data, or to retrieve the full JSON for each identifier. This is particularly important for verification of social media research.
The users command will return User metadata for the given screen names.
twarc users deray,Nettaaaaaaaa > users.jsonl
You can also give it user ids:
twarc users 1232134,1413213 > users.jsonl
If you want you can also use a file of user ids, which can be useful if you are
using the followers and friends commands below:
twarc users ids.txt > users.jsonl
The followers command will use Twitter's follower id API to collect the follower user ids for exactly one user screen name per request as specified as an argument:
twarc followers deray > follower_ids.txt
The result will include exactly one user id per line. The response order is reverse chronological, or most recent followers first.
Like the followers command, the friends command will use Twitter's friend id API to collect the friend user ids for exactly one user screen name per request as specified as an argument:
twarc friends deray > friend_ids.txt
The trends command lets you retrieve information from Twitter's API about trending hashtags. You need to supply a Where On Earth identifier (woeid) to indicate what trends you are interested in. For example here's how you can get the current trends for St Louis:
twarc trends 2486982
Using a woeid of 1 will return trends for the entire planet:
twarc trends 1
If you aren't sure what to use as a woeid just omit it and you will get a list
of all the places for which Twitter tracks trends:
twarc trends
If you have a geo-location you can use it instead of the woedid.
twarc trends 39.9062,-79.4679
Behind the scenes twarc will lookup the location using Twitter's trends/closest API to find the nearest woeid.
The timeline command will use Twitter's user timeline API to collect the most recent tweets posted by the user indicated by screen_name.
twarc timeline deray > tweets.jsonl
You can also look up users using a user id:
twarc timeline 12345 > tweets.jsonl
You can get retweets for a given tweet id like so:
twarc retweets 824077910927691778 > retweets.jsonl
If you want to get the replies to a given tweet you can:
twarc replies 824077910927691778 > replies.jsonl
Using the --recursive option will also fetch replies to the replies as well as
quotes. This can take a long time to complete for a large thread because of
rate limiting by the search API.
twarc replies 824077910927691778 --recursive
Unfortunately Twitter's API does not currently support getting replies to a tweet. So twarc approximates it by using the search API. Since the search API does not support getting tweets older than a week twarc can only get all the replies to a tweet that have been sent in the last week.
If you want you can use twarc programmatically as a library to collect
tweets. You first need to create a Twarc instance (using your Twitter
credentials), and then use it to iterate through search results, filter
results or lookup results.
from twarc import Twarc
t = Twarc(consumer_key, consumer_secret, access_token, access_token_secret)
for tweet in t.search("ferguson"):
print(tweet["text"])You can do the same for a filter stream of new tweets that match a track keyword
for tweet in t.filter(track="ferguson"):
print(tweet["text"])or location:
for tweet in t.filter(locations="-74,40,-73,41"):
print(tweet["text"])or user ids:
for tweet in t.filter(follow='12345,678910'):
print(tweet["text"])Similarly you can hydrate tweet identifiers by passing in a list of ids or a generator:
for tweet in t.hydrate(open('ids.txt')):
print(tweet["text"])In the utils directory there are some simple command line utilities for working with the line-oriented JSON, like printing out the archived tweets as text or html, extracting the usernames, referenced URLs, etc. If you create a script that you find handy please send a pull request.
When you've got some tweets you can create a rudimentary wall of them:
% utils/wall.py tweets.jsonl > tweets.html
You can create a word cloud of tweets you collected about nasa:
% utils/wordcloud.py tweets.jsonl > wordcloud.html
If you've collected some tweets using replies you can create a static D3
visualization of them with:
% utils/network.py tweets.jsonl tweets.html
Optionally you can consolidate tweets by user, allowing you to see central accounts:
% utils/network.py --users tweets.jsonl tweets.html
And if you want to use the network graph in a program like Gephi, you can generate a GEXF file with the following:
% utils/network.py --users tweets.jsonl tweets.gexf
gender.py is a filter which allows you to filter tweets based on a guess about the gender of the author. So for example you can filter out all the tweets that look like they were from women, and create a word cloud for them:
% utils/gender.py --gender female tweets.jsonl | utils/wordcloud.py > tweets-female.html
You can output GeoJSON from tweets where geo coordinates are available:
% utils/geojson.py tweets.jsonl > tweets.geojson
Optionally you can export GeoJSON with centroids replacing bounding boxes:
% utils/geojson.py tweets.jsonl --centroid > tweets.geojson
And if you do export GeoJSON with centroids, you can add some random fuzzing:
% utils/geojson.py tweets.jsonl --centroid --fuzz 0.01 > tweets.geojson
To filter tweets by presence or absence of geo coordinates (or Place, see API documentation):
% utils/geofilter.py tweets.jsonl --yes-coordinates > tweets-with-geocoords.jsonl
% cat tweets.jsonl | utils/geofilter.py --no-place > tweets-with-no-place.jsonl
To filter tweets by a GeoJSON fence (requires Shapely):
% utils/geofilter.py tweets.jsonl --fence limits.geojson > fenced-tweets.jsonl
% cat tweets.jsonl | utils/geofilter.py --fence limits.geojson > fenced-tweets.jsonl
If you suspect you have duplicate in your tweets you can dedupe them:
% utils/deduplicate.py tweets.jsonl > deduped.jsonl
You can sort by ID, which is analogous to sorting by time:
% utils/sort_by_id.py tweets.jsonl > sorted.jsonl
You can filter out all tweets before a certain date (for example, if a hashtag was used for another event before the one you're interested in):
% utils/filter_date.py --mindate 1-may-2014 tweets.jsonl > filtered.jsonl
You can get an HTML list of the clients used:
% utils/source.py tweets.jsonl > sources.html
If you want to remove the retweets:
% utils/noretweets.py tweets.jsonl > tweets_noretweets.jsonl
Or unshorten urls (requires unshrtn):
% cat tweets.jsonl | utils/unshorten.py > unshortened.jsonl
Once you unshorten your URLs you can get a ranked list of most-tweeted URLs:
% cat unshortened.jsonl | utils/urls.py | sort | uniq -c | sort -nr > urls.txt
Some further utility scripts to generate csv or json output suitable for
use with D3.js visualizations are found in the
twarc-report project. The
util directed.py, formerly part of twarc, has moved to twarc-report as
d3graph.py.
Each script can also generate an html demo of a D3 visualization, e.g. timelines or a directed graph of retweets.