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Some of the best tools for Twitter statistics

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Posted Sep 30 2009 12:35 PM

Twitter itself provided the earliest mechanisms for measuring Twitter use, in the form of member profile statistics. The running totals of friends, followers, and total contributions to the tweet corpus were all that early adopters and application developers focused on. Consequently, when the next wave of new users climbed on the Twitter train, they were clearly at a disadvantage: the available stats favored older accounts.

Throughout 2008, the sophistication of the metrics used to describe individual users evolved. Developers moved beyond using the information posted in profiles, and began taking into account the size and composition of a user's follow network as well as the rate and types of posts. The third-party applications discussed in this section offer a few ways to look at the stats that Twitter users can generate.

What's Your Tweet Worth?

The big question during the early life of the company was, "How is Twitter going to make money?" That question is still open, although an answer is expected in 2009. Inevitably, some members have followed it with a second question: "What's in it for me?"

Advertising on Twitter appears to be an emerging industry. Businesses have formed around selling advertising space on Twitter members' profile pages (Twittad), sponsorship of moments during live-tweeted events (such as Glam Media's Academy Awards coverage[1]), and even selling individual tweets (Magpie). To bring those dollars from advertisers to users, however, investors need some means of evaluating which members will provide the best return on investment.

Lava Row, an emergent social media consultancy firm in Iowa, built a tool specifically for this purpose. What's Your Tweet Worth? calculates how valuable you are to the Twitter community and spits out a price tag that you can put on your profile background for Twittad (@Twittad). Although the formula is hidden, the site returns the basic profile statistics, presumably as an indication that they are important in determining worth. If true, this application's contribution is to turn multiple metrics into a single, easy-to-digest dollar figure.


At the close of 2007, Damon Cortesi (@dacort) became curious about his own Twitter use and built a little Perl script that would scrape and digest the tweet footprint for a given account. About a month after he released it, he used that script as the base code for a project to teach himself Ruby on Rails, and that in turn became TweetStats.

This web tool examines archival data for a given user and aggregates information about when that user tweets into graphs for different dimensions. TweetStats also shows the people to whom you reply the most and the various ways you publish your status updates. TweetStats graphs are a nice way to understand one's longitudinal usage of Twitter. Cortesi has also added a trends tracker, which tracks the top 10 keywords returned by Twitter's trends API over time. The site includes a historic look at what has been important to twitterers via a tag cloud that includes such terms as iPhone, Obama, and Christmas.

Examining your Twitter history with TweetStats can provide some interesting insights into your daily routine. For example, for the six hours between midnight and dawn, my wife has almost no Twitter activity. Any time she spends conscious during that period is reserved for mild insomnia and needy kids. On the other hand, I have a spike in activity just as my wife is winding down for the night, followed by a low but steady murmur throughout the night. This reflects not only my general lack of sleep but also my irregular sleeping patterns (sometimes I stay up late, sometimes I get up early). It makes me wonder whether TweetStats could be used to reveal patterns in couples' use of Internet technologies.

Follow Cost

Whenever I am notified by email that I have a new follower, I do the same few things. First, I pull up the user's profile page and look for a real name and bio information. The more complete and human the profile looks, the more likely I am to keep looking at the rest of the page. I then glance at the statistics for signs of a spammer: high following counts coupled with low follower counts are a red flag. Finally, I start looking at the quality of the member's tweets, shying away from people who only post replies or automated tweets. Not everyone values the same things, but most people have some kind of system they use to determine whether or not to reciprocate.

Follow Cost facilitates this process: it attempts to assign an aggregated metric to your Twitter use to give other people another way of evaluating your value. This site only measures tweeting activity. The unit of measurement, however, is defined in a creative way: using über-user Robert Scoble (@scobleizer) as the baseline. According to creators Luke Francl (@lof) and Barry Hess (@bjhess), as of late September 2008 Scoble had tweeted 14,319 times in 675 days, for an average of 21.21 tweets per day. One milliscoble—their chosen unit of measure—is defined as 1/1,000th of the average daily Twitter status updates by Robert Scoble, or .02121 tweets per day.

The evaluation tool also has some hidden features. For instance, shortly after the 2008 U.S. elections, when I spent most of the day and night tracking the Electoral College updates on Twitter. It was a personal record for number of tweets in a day, almost all of which were about voting and the election. Follow Cost picked up on that surge, raising my longer-range stats by 3–4 daily tweets and flagging me as a political junkie. It makes me wonder if I would get a background of footballs instead of flags if I spent a Sunday afternoon tweeting play-by-play for the Chicago Bears.

Follow Cost has at least one more surprise. When I checked, the follow cost for Robert Scoble showed a high level of activity for his last 100 tweets, with a rating of 76.46 posts per day, or over 3,600 milliscobles. The background for that stat was a mushroom cloud, and the title changed to "nuclear follow cost."

Twitter Grader

One of the more popular statistical tools to pop up recently is Twitter Grader, an evaluation site that accumulates several stats and turns them into a single grade on a 100-point scale. Built by the founders of HubSpot, which offers similar graders for websites and press releases, Twitter Grader collects data and adjusts your standing based on your data set. After about a month of service, it had already collected 300,000 profiles.

The grade it gives is the sum of several factors, including the number of followers you have, the power of those followers in the network, how often you update, and how thoroughly you filled out your profile. The single-figure statistic is nice because it is easy to wrap your head around. The metaphor of a test grade, though, unfortunately implies that any number lower than 60 is a failure. Twitter Grader, Twinfluence, and a few other member-ratings tools have drawn criticism for assigning values that some believe are best defined by individual users rather than algorithms.

It isn't the grade that makes this site so interesting, though—HubSpot added a few other features to go with the rating and profile stats. The tag cloud isn't that useful, probably because it doesn't go deep enough into your tweet history to find much useful information to summarize in that way. The list of suggested people to follow, on the other hand, has had an impact on Twitter users. Before Twitter Grader, the people choosing to follow me were largely confined to three groups: people I knew, people from my home state, and spammers. After this app hit the scene, a fourth group became prominent: active users with moderately sized follow networks and some relevant tweet content. I suspect this spike in random follows is in fact not so random, but rather is attributable to people using Twitter Grader to find new members to follow.


Sometimes the best inspiration comes from an example of what not to do. One day in early November 2008, the Twitter community took a new third-party application to task, shortly after openly embracing it. At first Twitter was ablaze with signs of interest in the form of self-promoting tweets about a new user-ranking site, but only a few hours later came the backlash, as many speculated about whether the simple application was really a phishing scam. Panic ensued, especially after ZDNet's Oliver Marks posted an article about user gullibility.[2] Passwords were changed. Twitterank's programmer responded. A parody was created.[3] Twitter spent the latter part of the day responding to user complaints about something it wasn't responsible for, and the Internet had a nice little meme about trust and authentication.

Twitterank was billed as a "PageRank for Twitter users," claiming to ignore follower counts and instead use other indicators to gauge importance in the network. The initial page looked hastily assembled and was littered with a lot of tech slang; it warned users to take caution when giving away their Twitter passwords⁠—and then asked them to enter their passwords. The ROI for granting your trust to this site was a page with a cryptic number and nothing more.

To his credit, the creator of Twitterank—Ryo Chijiiwa (@ryochiji), a programmer at Google who made this tool as a side project—responded to the criticism and histrionics about his site being a phishing attack with some fast changes. Not the least of these were adding a method for calculating rank without requiring a password, albeit less accurately; adding context (he included a percentile to let you know where your number fit into the known Twitterverse); and a link to his identity. Those are the types of clues people look for when determining whether to trust a site.

The Twitterank incident came two months before a real phishing attack on the Twitter community that used direct messages from known friends.

The Twitterank of yesterday certainly won't be the Twitterank of tomorrow. It is unknown whether Chijiiwa's project will evolve into something useful or permanent, but it is clear that the debut could have gone better. The beleaguered developer tweeted at the end of his long day: "I have a new-found appreciation for people who do PR."[4]

[1] From the February 22, 2009 blog article, Glam edits Oscars Twitter feed and makes money, by Matt Marshall, published on VentureBeat.

[2] From the November 12, 2008 blog article Gullible Twitter users hand over their usernames and passwords - did you get your Twitterank yet?!, by Oliver Marks, published on ZDNet.

[3] Twitter AWESOMENESS!!! duplicates the tone and design of the initial Twitterank web page. In the source code was a comment: "<!-- And if you're reading this, then congrats – you're more savvy than the average twitter-bear! -->".

[4] Ryo Chijiiwa's status update.

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