Abstract
It has been shown that people attempt to accomplish a variety of intentions during the course of an information seeking session, and there is reason to believe that these different information seeking intentions can benefit from system support tailored to each such intention. We address the problem of predicting the presence of such intentions during an information seeking session, through analysis of observable user search behaviors. We present results of a study of 40 participants, each working on two different journalism tasks, which investigated how their search behaviors could indicate their intentions. Using 725 query-segments captured from this study, we demonstrate that information seeking intentions can be predicted with a simple classification model using a linear combination of search behavior features that can be logged with a browser plug-in.