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)

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