User Follower Distribution

Account Creation Timeline

Retweet Networks

by Devin Gaffney, Ian Pearce, Max Darham, and Max Nanis

Hello world. It’s been awhile since the Web Ecology community last made a peep on the web. Some had been speculating that we had simply up and disappeared, but reports of our demise were greatly exaggerated, as they say.

Here’s what we’ve got.

Thanks to the completely amazing work of our affiliate researchers at Bennington, we’re glad today to announce the public launch of 140Kit, Web Ecology’s very own free-to-use toolkit for exploring and data mining Twitter. It’s the final product of the various provisional tools we’ve used to produce our previous reports on the social phenomena of Twitter, and of lead researcher Devin Gaffney’s own work on high throughput humanities.

So what does it do? Notably:

• It enables complete data pulls for a set of users or terms on Twitter, with searches running continuously.
• The ability to download those data pulls in raw form to use for whatever you please.
• The ability to stand on the shoulders of giants by mixing and matching existing data pulls to generate entirely new combinations of data and analysis.
• And the ability to instantaneously generate basic visualizations around the data (term use, inequality of participation, etc).

Best of all, we are making this platform open and free to use for all interested users. This includes opening up an API for queries of all sorts, an honest, open, and editable codebase, and plans already in the works to make the program extensible to allow developers to write their own analytics for the kit, on whatever sort of metrics in whatever programming language (stay tuned for details).

So, get in there and play, people. And let us know if you have any questions! tim.hwang@webecologyproject.org or contact@webecologyproject.org.

by Alex Leavitt & Tim Hwang

with Patrick Davison, Mike Edwards, Devin Gaffney, Sam Gilbert, Erhardt Graeff, Jennifer Jacobs, Dan Luxemburg, Kunal Patel, Mike Rugnetta, & Karina van Schaardenburg

This paper represents an initial study of ChatRoulette.com, conducted between February 6th and 7th, 2010 by researchers in attendance at Web Ecology Camp III in Brooklyn, NY. We sampled 201 ChatRoulette sessions, noting characteristics such as group size and gender. We also conducted 30 brief interviews with users to inquire about their age, location, and frequency of ChatRoulette use.

Summary

• ChatRoulette represents an example of a probabilistic community: a community shaped by a platform which mediates the encounters between its users by eliminating lasting connections between them.

• After ChatRoulette users become more acquainted with the system (ie., do not browse solely to explore), we predict a decrease in explicit content, an increase in the consolidation of content genres, and an increase in the formation of celebrity figures.

• Our survey shows that ChatRoulette’s current community continues to consist of males age 18-24, concurrent with Alexa data.

You can download our report here.

By Jon Beilin

One of the tenets of Web Ecology is accessibility to the field through open tools and open data. At the Web Ecology Project, we’re working to get more of our code in a clean, commented, and releasable state. The first tool that we have queued up for release is a Python module allowing easy use of Google Language Tools, involving language detection and translation, with transliteration in an experimental state (Google has not yet released the API spec for the transliteration portion so that was reverse-engineered).

Now for some sample uses of the tool:

>> from googlelanguage import *

>> print lang_translate("comment dit on 'WebEcology' en francais?", dest_lang="en")
{'translatedText': 'how it says 'WebEcology' in French?', 'detectedSourceLanguage': 'fr'}


We used it ourselves to detect the language of each tweet in a sample of 1 million tweets from our database, with the following results:

We’ve also found it easy to combine the tool with SQLAlchemy to create metadata tables with linguistic information.

It is our hope that this small, MIT/X11-licensed release will prove useful to some in the Web Ecology community. Until we figure out which platform we’re going to use for open repository hosting, you can download the file here. And if you would like to contribute patches or additions, or if you have any questions, feel free to send them to Jon.Beilin@webecologyproject.org

I would also like to thank Sam Gilbert for his invaluable contributions, feedback, and support.

By Erhardt Graeff
with Seth Woodworth

Data Summary

• 111,741 tweets about Afghanistan and its presidential election posted between August 11, 2009 and September 9, 2009
• 11,255 tweets on August 20, 2009, the day of the election
• 29,642 users talked about Afghanistan in our dataset
• Top 10% of tweeters contributed 65% of tweets (same as Iran Election)
• Number of retweets for a user was not correlated to their tweeting volume (same as Iran Election)
• 483 hashtags were used at least 3 times
• No single, dominant hashtag (differs from Iran Election)
• 3 most used hashtags: #Afghan09, #Afghanistan, and #AfghanElection

Introduction

Afghan citizens went to the polls on August 20, 2009 after a controversial delay recommended by Afghanistan’s Independent Election Commission to allow ample time to prepare for fair and safe elections. Karzai was favored to win the election amid a large pool of contending candidates; the most serious challenge coming from former Foreign Minister of Afghanistan Abdullah Abdullah. In pre-election polling, Abdullah gained significant momentum as election day drew nearer and other candidates dropped their campaigns.

In a clear reference to the protests following the June presidential election in Iran, Abdullah’s campaign manager was quoted predicting street violence if Abdullah doesn’t win. Here at the Web Ecology Project, we wondered if Twitter would play as significant a role in reporting the election as it did in Iran. In a country where mobile phone subscriptions add up to an estimated 50% of the population, but internet access was roughly 1.5% at last estimate with the status of network expansion [pdf] unclear, could the available ICT infrastructure and awareness of social media prompted by the “twitter revolution” in Iran enable a similar phenomenon post-August 20?

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By Alex Leavitt
with Evan Burchard, David Fisher, & Sam Gilbert

Using a new methodology based on the content and responses of 12 popular users, we determined measurements of relative influence on Twitter. We examined an ecosystem of 134,654 tweets, 15,866,629 followers, and 899,773 followees, and in response to the 2,143 tweets generated by these 12 users over a 10-day period, we collected 90,130 responses published by other users.

Summary of Findings

An analysis of our methodology and statistics suggests that on Twitter, among various configurable conclusions:

• mashable is more influential than CNN.
• sockington is more influential than MCHammer, while MCHammer is more influential than three major social media analysts (garyvee, Scobleizer, and chrisbrogan).
• Celebrities with higher follower totals (eg., THE_REAL_SHAQ and ijustine) foster more conversation than provide retweetable content.
• News outlets, regardless of follower count, influence large amounts of followers to republish their content to other users.

Click to expand image. A larger version with more temporal depth is linked at the bottom of this report.

We would also like to thank Jon Beilin, Mac Cowell, and Tim Hwang for their invaluable contributions, feedback, and support.

The Influentials (pdf)

10 Days of Influence Tracked by Density of Responses (2993.27 KB jpg)
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When deciding whether someone is worth following or talking to on Twitter, most of us make a snap judgment based on a user’s follower count, but what does this really tell us?

For our fourth publication, the Web Ecology Project decided to move beyond follower count to find a better way to measure influence on Twitter. Focusing in on a handful of celebrities, news outlets, and social media experts widely perceived to be among the Twitter elite, we looked at the extent to which each of these users can:

• Spread content through twitter by generating Retweets and Via’s
• Foster conversation by generating @s and Replies

The results, taken from 10 days of Twitter activity (August 15th through August 24th), were surprising. Consider how the users we looked at rank by follower count:

When you look at the extent to which any given tweet can spread content or foster conversation, these rankings change significantly:

iJustine, for example, can spread more content than MCHammer, who has over twice as many followers, and in terms of generating conversation celebrities like THE_REAL_SHAQ and aplusk tower dominate news outlets and social media experts alike.

When you look at how much each of these users is able to generate conversation and spread content relative to their follower counts, however, the rankings shift even more dramatically:

Values for MCHammer, aplusk, THE_REAL_SHAQ and CNNbrk plummet, while the social media experts, especially Chris Brogan, become powerful players.

These figures are just a taste of what’s to come. In our full report, we’ll unpack these numbers further and explore the somewhat surprising nuances and types of influence on Twitter.

By Elsa Kim and Sam Gilbert
with Michael J. Edwards and Erhardt Graeff

Michael Jackson’s death created an emotional outpouring of unprecedented magnitude on Twitter. In this report, we examine 1,860,427 tweets about Jackson’s death in order to test various methods of sentiment analysis and gain insights into how people express emotion on Twitter.

Key findings

• At its peak, the conversation about Michael Jackson’s death on Twitter proceeded at a rate of 78 tweets per second.
• Users tweeting about Jackson’s death tend to use far more words associated with negative emotions than are found in ‘everyday’ tweets.
• Roughly 3/4 of tweets about Jackson’s death that use the word “sad” actually express sadness, suggesting that sentiment analysis based on word usage is fairly accurate.
• That said, there is extensive disagreement between human coders about the emotional content of tweets, even for emotions that we might expect would be clear (like sadness).
• Tweets expressing personal, emotional sadness about the Jackson’s death showed strong agreement among coders while commentary on the auxiliary social effects of Jackson’s death showed strong disagreement.
• We argue that this pattern in the “understandability” of certain types of communication across Twitter is due to the way the platform structures the expression of its users.

We would like to thank Jonathan Beilin, Evan Burchard, David Fisher, Tim Hwang, Alex Leavitt, Dharmishta Rood, Max van Kleek, Jue Wang, and Seth Woodworth for their invaluable feedback and support.

Detecting Sadness in 140 Characters (pdf), Appendices (pdf)

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The Iranian Election on Twitter (pdf)