Everybody tweets these days but not all tweets are created equal, and neither are tweeters for that matter. One of the truest measures of a tweet and, by proxy, the tweeter, is how far into the social-graph the message propagates via retweeting. Retweeting is an explicit indication of engagement on the part of the retweeter and in most cases, an endorsement of the message and the original author. That all said, it is an interesting exercise to see where in the social-graph a particular tweet reached. The Twitter API makes it easy to tell how many times and by whom a message was retweeted but it takes a bit more legwork to determine the path taken to the recipients.
A simple method to follow the propagation of a tweet is to do a breadth-first traversal of followers links, starting at the message author, until all retweeters have been accounted for. Obviously there are some assumptions wrapped up in this methodology but for the most part evidence supports the results. The Python script below performs this walk through the social graph. For economy against the Twitter API, the script caches follower lists in a Redis server so that they may be re-used for subsequent runs. This scheme works best when examining tweets which are closely related and incorporate many of the same Twitter users.
For visualization purposes, the Python script outputs a JSON file for consumption by a D3 force-directed graph template. D3 expects nodes and links enumerated in separate lists, the link elements making reference to the node elements via node list indices.
While the ability to gather broad insight with this method is limited by Twitter API rate controls, it could be used to do a focused study on a specific Twitter user, looking for prominent social-graph pathways and individuals that warrant reciprocation. Failing that, the D3 transitions as the graph builds and stabilizes makes fascinating viewing.
To get it running on your machine, do the following: