Track: Data Mining
Why We Search: Visualizing and Predicting User Behavior
- Eytan Adar (University of Washington, CSE)
- Daniel Weld (University of Washington, CSE)
- Brian Bershad (University of Washington, CSE)
- Steven Gribble (University of Washington)
The aggregation and comparison of behavioral patterns on the WWW represent a tremendous opportunity for understanding past behaviors and predicting future behaviors. In this paper, we take a first step at achieving this goal. We present a large scale study correlating the behaviors of Internet users on multiple systems ranging in size from 27 million queries to 14 million blog posts to 20,000 news articles. We formalize a model for events in these time-varying datasets and study their correlation. We have created an interface for analyzing the datasets, which includes a novel visual artifact, the DTWRadar, for summarizing differences between time series. Using our tool we identify a number of behavioral properties that allow us to understand the predictive power of patterns of use.