Tauhid R. Zaman, Ralf Herbrich, Jurgen Van Gael, and David Stern
We present a new methodology for predicting the spread of information in a social network. We focus on the Twitter network, where information is in the form of 140 character messages called tweets, and information is spread by users forwarding tweets, a practice known as retweeting. Using data of who and what was retweeted, we train a probabilistic collaborative filter model to predict future retweets. We find that the most important features for prediction are the identity of the source of the tweet and retweeter. Our methodology is quite flexible and be used as a basis for other prediction models in social networks.
In Computational Social Science and the Wisdom of Crowds Workshop (colocated with NIPS 2010)