References for my IZEAfest talk

I gave a talk at IZEAfest about the science of sharing. I wove together a narrative based on recent research in social network analysis and some work we’ve done at People Pattern. It was fun preparing and delivering it!

The livestream of the talk is here [NOTE: the video is currently unavailable and I’m working on finding a new link], and here are the slides.


In preparing for the talk, I looked at and referred to a lot of papers. Here’s a list of papers referenced in the talk! Each paper is followed by a link to a PDF or site where you can get the PDF. A huge shout-out to the fantastic work by these scholars—I was really impressed by social network analysis work and the questions and methods they have been working on.

Chen et al. (2015). “Making Use of Derived Personality: The Case of Social Media Ad Targeting.” –

Cheng et al. (2015). “Antisocial Behavior in Online Discussion Communities.” –

Friggeri et al. (2015). “Rumor Cascades.” –

Goel et al. (2015). “The Structural Virality of Online Diffusion.” –

Gomez-Rodriguez et al. (2014). “Quantifying Information Overload in Social Media and its Impact on Social Contagions.” –

Gomez Rodriguez et al. (2014). “Uncovering the structure and temporal dynamics of information propagation.” –

Iacobelli et al. (2015). “Large Scale Personality Classification of Bloggers.” –

Kang and Lerman (2015). “User Effort and Network Structure Mediate Access to Information in Networks.” –

Kooti et al. (2015). “Evolution of Conversations in the Age of Email Overload.” –

Kulshrestha et al (2015). “Characterizing Information Diets of Social Media Users.”  –

Lerman et al. (2015). “The Majority Illusion in Social Networks.” –

Leskovec et al. (2009). “Meme-tracking and the Dynamics of the News Cycle.” –

Niculae et al. (2015). “QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns.” –

Weng et al. (2014). “Predicting Successful Memes using Network and Community Structure.” –

Yarkoni (2010). “Personality in 100,000 Words: A large-scale analysis of personality and word use among bloggers.” –

A walk-through for the Twitter streaming API

Topics: Twitter, streaming API


Analyzing tweets is all the rage, and if you are new to the game you want to know how to get them programmatically. There are many ways to do this, but a great start is to use the Twitter streaming API, a RESTful service that allows you to pull tweets in real time based on criteria you specify. For most people, this will mean having access to the spritzer, which provides only a very small percentage of all the tweets going through Twitter at any given moment. For access to more, you need to have a special relationship with Twitter or pay Twitter or an affiliate like Gnip.

This post provides a basic walk-through for using the Twitter streaming API. You can get all of this based on the documentation provided by Twitter, but this will be slightly easier going for those new to such services. (This post is mainly geared for the first phase of the course project for students in my Applied Natural Language Processing class this semester.)

You need to have a Twitter account to do this walk-through, so obtain one now if you don’t have one already.

Accessing a random sample of tweets

First, trying pulling a random sample of tweets using your browser by going to the following link.


You should see a growing, unwieldy list of raw tweets flowing by. It should look something like the following image.


Here’s an example of a “raw” tweet (which comes in JSON, or JavaScript Object Notation):

[sourcecode language=”json”]
{"text":"#LetsGoMavs til the end RT @dallasmavs: Are You ALL IN?","truncated":false,"retweeted":false,"geo":null,"retweet_count":0,"source":"web","in_reply_to_status_id_str":null,"created_at":"Wed Apr 25 15:47:39 +0000 2012","in_reply_to_user_id_str":null,"id_str":"195177260792299521","coordinates":null,"in_reply_to_user_id":null,"favorited":false,"entities":{"hashtags":[{"text":"LetsGoMavs","indices":[0,11]}],"urls":[],"user_mentions":[{"indices":[27,38],"screen_name":"dallasmavs","id_str":"22185437","name":"Dallas Mavericks","id":22185437}]},"contributors":null,"user":{"show_all_inline_media":true,"statuses_count":3101,"following":null,"profile_background_image_url_https":"","profile_sidebar_border_color":"eeeeee","screen_name":"flyingcape","follow_request_sent":null,"verified":false,"listed_count":2,"profile_use_background_image":true,"time_zone":"Mountain Time (US & Canada)","description":"HUGE ROCKETS & MAVS fan. Lets take down the Lakers & beat up on the East. Inaugural member of the FC Dallas – Fort Worth fan club.","profile_text_color":"333333","default_profile":false,"profile_background_image_url":"","created_at":"Thu Oct 21 15:40:21 +0000 2010","is_translator":false,"profile_link_color":"1212cc","followers_count":35,"url":null,"profile_image_url_https":"","profile_image_url":"","id_str":"205774740","protected":false,"contributors_enabled":false,"geo_enabled":true,"notifications":null,"profile_background_color":"0a2afa","name":"Mandy","default_profile_image":false,"lang":"en","profile_background_tile":true,"friends_count":48,"location":"ATX / FDub. From Galveston !","id":205774740,"utc_offset":-25200,"favourites_count":231,"profile_sidebar_fill_color":"efefef"},"id":195177260792299521,"place":{"bounding_box":{"type":"Polygon","coordinates":[[[-97.938383,30.098659],[-97.56842,30.098659],[-97.56842,30.49685],[-97.938383,30.49685]]]},"country":"United States","url":"","attributes":{},"full_name":"Austin, TX","country_code":"US","name":"Austin","place_type":"city","id":"c3f37afa9efcf94b"},"in_reply_to_screen_name":null,"in_reply_to_status_id":null}

There is a lot of information in there beyond the tweet text itself, which is simply “#LetsGoMavs til the end RT @dallasmavs: Are You ALL IN?” It is basically a map from attributes to values (and values may themselves be such a map, e.g. for the “user” attribute above). You can see whether the tweet has been retweeted (which will be zero when the tweet is first published), what time it was created, the unique tweet id, the geo-coordinates (if available), and more. If an attribute does not have a value for the tweet, it is ‘null’.

I will return to JSON processing of tweets in a later tutorial, but you can get a head start by seeing my tutorial on using Scala to process JSON in general.

Command line access to tweets

Assuming you were successful in being able to view tweets in the browser, we can now proceed to using the command line. For this, it will be convenient to first set environment variables for your Twitter username and password.

[sourcecode language=”bash”]
$ export TWUSER=foo
$ export TWPWD=bar

Obviously, you need to provide your Twitter account details instead of foo and bar…

Next, we’ll use the program curl to interact with the API. Try it out by downloading this blog post.

[sourcecode language=”bash”]
$ curl > bcomposes-twitter-api.html
$ less bcomposes-twitter-api.html

Given that you pulled tweets from the API using your web browser, and that curl can access web pages in this way, it is simple to use curl to get tweets and direct them straight to a file.

[sourcecode language=”bash”]
$ curl -u$TWUSER:$TWPWD > tweets.json

That’s it: you now have an ever-growing file with randomly sampled tweets. Have a look and try not to lose your faith in humanity. 😉

Pulling tweets with specific properties

You might want to get the tweets from specific users rather than a random sample. This requires user ids rather than the user names we usually see. The id for a user can be obtained from the Twitter API by looking at the /users/show endpoint. For example, the following gives my information:


Which gives:

[sourcecode language=”xml”]

<name>Jason Baldridge</name>
<location>Austin, Texas</location>
Assoc. Prof., Computational Linguistics, UT Austin. Senior Data Scientist, Converseon. OpenNLP developer. Scala, Java, R, and Python programmer.


So, to follow @jasonbaldridge via the Twitter API, you need user id 119837224. You can pull my tweets via the API using the “follow” query parameter.

[sourcecode language=”bash”]
$ curl -d follow=119837224 -u$TWUSER:$TWPWD

There is a good chance I’m not tweeting right now, so you’ll probably not see anything. Let’s follow more users, which we can do by adding more id’s separated by commas.

[sourcecode language=”bash”]
$ curl -d follow=1344951,5988062,807095,3108351 -u$TWUSER:$TWPWD

This will follow Wired Magazine (@wired), The Economist (@theeconomist), the New York Times (@nytimes), and the Wall Street Journal (@wsj).

You can also write those ids to a file and read them from the file. For example:

[sourcecode language=”bash”]
$ echo "follow=1344951,5988062,807095,3108351" > following
$ curl -d @following -u$TWUSER:$TWPWD

You can of course edit the file “following” rather than using echo to create it. Also, the file name can be named whatever you like (“following” as the name is not important here).

You can search for a particular term in tweets, such as “Scala”, using the “track” query parameter.

[sourcecode language=”bash”]
$ curl -d track=scala -u$TWUSER:$TWPWD

And, no surprise, you can search for multiple items by using commas to separate them.

[sourcecode language=”bash”]
$ curl -d track=scala,python,java -u$TWUSER:$TWPWD

However, this only requires that a tweet match at least one of these terms. If you want to ensure that multiple terms match, you’ll need to write them to a file and then refer to that file. For example, to get tweets that have both “sentiment” and “analysis” OR both “machine” and “learning” OR both “text” and “analytics”, you could do the following:

[sourcecode language=”bash”]
$ echo "track=sentiment analysis,machine learning,text analytics" > tracking
$ curl -d @tracking -u$TWUSER:$TWPWD

You can pull tweets from a specific rectangular area (bounding box) on the Earth’s surface. For example, the following pulls geotagged tweets from Austin, Texas.

[sourcecode language=”bash”]
$ curl -d locations=-97.8,30.25,-97.65,30.35 -u$TWUSER:$TWPWD

The bounding box is given as latitude (bottom left), longitude (bottom left), latitude (top right), longitude (top right). You can add further bounding boxes to capture more locations. For example, the following captures tweets from Austin, San Francisco, and New York City.

[sourcecode language=”bash”]
$ curl -d locations=-97.8,30.25,-97.65,30.35,-122.75,36.8,-121.75,37.8,-74,40,-73,41 -u$TWUSER:$TWPWD


It’s all pretty straightforward, and quite handy for many kinds of tweet-gathering needs. One of the problems is that Twitter will drop the connection at times, and you’ll end up missing tweets until you start a new process. If you need constant monitoring,  see UT Austin’s Twools (Twitter tools) for obtaining a steady stream of tweets that picks up whenever Twitter drops your connection.

In a later post, I’ll detail how to use an API like twitter4j to pull tweets and interact with Twitter at a more fundamental level.