Strokes of insight: User intent detection and kinematic compression of mouse cursor trails

We analyzed more than 10,000 browsing sessions comprising about 5 million of data points, and compared different segmentation techniques to detect discrete cursor chunks that were then reconstructed with the ΣΛM. Our main contribution is thus a novel methodology to automatically tell chunks with and without intention apart. We also contribute with kinematic compression, a novel application to compress mouse cursor data while preserving most of the original information. Ultimately, this work enables a deeper understanding of mouse cursor movements production, providing an informed means to gain additional insight about users' browsing behavior.