Last semester i was part of a rather interesting experiment through the DTU course “02805 Social Graphs and Interactions”
The point of the experiment where as described below by Sune Lehmn
Is it possible for a small computer science course to exert measurable influence (trending topics) on Twitter, a massive social network with hundreds of millions of users? The surprising answer to that question is “yes”. That’s exactly what we did this year, using simple Python scripts and the Twitter API. Below we explain why & how + some of our findings along the way.
Read the rest of Sunes blog post here.
You’re here because of a robot
Our twitter-bot @happytoaster later to be renamed to @jinxymulan, was the winner in the course competition on getting the most followers.
Jinxy Mulan in Boston
Most interesting were the last part of the project were the bots moved to Boston. Here our twitter bot gathered data from the social network around 5 Boston based comic shops and their followers.
One of several result that were extracted from the dataset was a classifier for Commercial and non-commercial tiwtter accounts in the network. The classifier achieved an accuracy of around 80%.
Figure 1 Graph of the social network connection categorised by our classifier. Red nodes are commercial profiles, cyan nodes are personal profiles. Purple are unclassified The nodes are sized accordingly to their number of followers.
Contact me if you are interested in the full report.