DCortesi . blog

Tweetie has become my exclusive application on the iPhone for Twitter usage. Between multiple account support, saved searches, and a quick, simple interface it replaced the few other apps I had to use simultaneously to achieve all of these.

As part of the Twitter application stats I maintain over at TweetStats, I would notice Tweetie usage increasing on the weekends. My assumption is that mobile use rises on the weekends as people are not on their work computers.

As part of a potential new service offering at TweetStats (activity graphs for your app!), I decided to verify my assumption*. As you can see, there’s a definite increase in usage of Tweetie on the weekends (gray bars).

Just for comparison sake, let’s take a look at another popular mobile app, TwitterFon. We see the same characteristics, with surprisingly similar trends.

To hit the point home, here’s a graph showing TweetDeck usage (the most popular desktop client) where we see a slight decline in usage during the weekend over the prior few days. However, usage is still comparable to the beginning of the work week.

And finally, a brief comparison of Tweetie and TweetDeck and their respective usage.

Mobile and Desktop application usage over the course of two weeks

*These statistics are generated using data collected at TweetStats utilizing the fantastic Gnip service and represent the large majority of updates posted on Twitter. I do not collect statistics on protected updates.

I just realized I never posted here about my recent Twitter Bio Search Tool, TweepSearch. I guess I’ve just been a little heads down lately. I’ve been busy with various Twitter apps, work, and I’ll be heading to Boston tomorrow for the SOURCE con and to visit with friends and family.

Anyway, TweepSearch - the original idea of the site was to allow somebody on Twitter to search the bios of their followers. It was inspired by a tweet from @SethSimonds and you can read more on the About page. However, once I started building it, I realized I was creating a more generic Twitter bio search application. I just updated the application yesterday and it now allows you to login (non-SSL, I’ll be fixing that eventually) and (un)follow directly from the interface, searches all Twitter profile fields by default, and allows you to search your friends and followers. Some examples:

Search for security peeps in Seattle: location:seattle security
Search my friends and followers for security peeps: @dacort security
Search my friends for peeps in Boston: @dacort only:friends location:boston
Except for those folks I’ll see at the con ;): @dacort only:friends location:boston -hacker -security

As you can see, the search syntax is pretty extensive. I’d like to add geo-based searches in the future as the full-text indexing engine I’m using supports it. Thinking Sphinx, the Rails plugin for Sphinx is also amazing and I have to thank EC2 for allowing me to scale so quickly when the site first got hammered due to a great post on louisgray.com courtesy of Jesse Stay.

I’m currently just over 1.4 million Twitter profiles indexed and constantly growing.

Have I mentioned I like poking through data before? Maybe a few hours ago? Well I do. And while reviewing a book this evening on how to build applications with the Twitter API, I was motivated to take a look at usage of the “Favorites” feature on Twitter.

Below is a graph showing the count of favorites across approximately 135,000 Twitter users. As expected, the majority of users have below 5,000 favorites.

Digging in a little more, I got curious if there was any correlation between the number of friends you have and the amount of favorites. Below we see a subsection of the favorites to friends ratio. Interestingly, an increase in friends actually correlates in a decrease in the usage of the favorites function. But wait, there’s an unusual spike in there.

Zooming in just a little bit more and we see an interesting pattern around the Twitter 2k Effect again. Seems like once people hit the 2,000 friends limit, they start paying more attention to people’s tweets?

And finally, curious who those crazy favoriting users are? Here are the top 10 from my data set, which is definitely not authoritative, but interesting nonetheless.

Thanks again to Tableau (@Tableau on Twitter) for making it easy to slice and dice the data.

As part of a recent project, I’ve been digging into some pretty cool data using Tableau. One of the instant deciders somebody on Twitter makes when they’re followed by a new user is their friends/followers ratio. If a user has lots of friends, but few followers, they’re not likely to be very interesting or can even be spammy accounts.

As part of their attempt to combat spam, Twitter initially limits the number of people you can follow to 2,000. Once you have been vetted by other users in the form of them following you, you can add more friends. This creates an interesting distribution when you start analyzing the friends to followers count. Taking a look at the image below, there are several things to note.

• There’s a large majority of Twitter users within the initial friend/following block of 2,000
• People rarely have over 1,000 friends without at least 250 people following them back
• You can obviously see that Twitter allows you to start adding more friends once you’ve hit 1,800 followers
• Once that limit has been passed, people generally continue to have a fairly steady ratio of 1:1
• However, there are a fair number of users who begin to restrict their # of friends after that point, but continue to receive more followers once they’ve been “acknowledged”
• Most of the users with more friends than followers in the bottom right are early Twitter accounts before Twitter imposed their limit
• There also seems to be a significant group of celebrity or otherwise popular users that have limited friends, but stretch up the left side with a large number of followers

What other conclusions do you draw from this? There are some other interesting behaviors once you dive into the 2k section.