Define a similarity metric and use it to compare or further analyze two Twitter users or groups of users. You’ll essentially be developing a profile and then measuring how well a particular user fits that profile. For example, do certain hashtags, keywords (e.g., LOL, OMG, etc.), or similar metrics appear in #JustinBieber tweets more than in more intellectual tweets like ones associated with #gov20? This question might seem obvious, but what interesting things might you learn along the way while calculating a quantifiable answer?

Which celebrity Twitterers have a very low concentration of tweet entities yet a high tweet volume? Does this make them ramblers—you know, those folks who tweet 100 times a day about nothing in particular? Conversely, which celebrity Twitterers have a high concentration of hashtags in their tweets? What obvious comparisons can you make between the two groups?

If you have a Twitter account and a reasonable number of tweets, consider analyzing yourself and comparing your tweet streams to those of other users. How similar are you to various Twitterers along a particular spectrum?

We didn’t broach the subject of Twitter’s lists API. Can you profile various members of a list and try to discover latent social networks? For example, can you find a metric that allows you to approximate political boundaries between followers of the @timoreilly/healthcare and @timoreilly/gov20 lists?

It might be interesting to analyze the volume of tweets or content of tweets by time of day. This is a pretty simple exercise and could incorporate the SIMILE Timeline—discussed in Visualizing Mail “Events” with SIMILE Timeline—for easy visualization.

Near real-time communication is increasingly becoming the norm and Twitter provides a streaming API, which provides convenient access to much of their “fire hose.” See the online streaming documentation and consider checking out tweepy as a terrific streaming client.