SenseSeek Dataset: Multimodal Sensing to Study Information Seeking Behaviors
Kaixin Ji, Danula Hettiachchi, Falk Scholer, Flora D. Salim, Damiano Spina

TL;DR
SenseSeek is a pioneering multimodal dataset capturing physiological and behavioral data during complex information search tasks, enabling deeper understanding of cognitive processes involved in information seeking.
Contribution
This paper introduces the first dataset combining physiological signals and interaction data during multi-stage information search tasks using consumer sensors.
Findings
Baseline analysis shows sensor data varies with cognitive intent.
Data effectively discriminates different search stages.
Physiological signals correlate with information seeking behaviors.
Abstract
Information processing tasks involve complex cognitive mechanisms that are shaped by various factors, including individual goals, prior experience, and system environments. Understanding such behaviors requires a sophisticated and personalized data capture of how one interacts with modern information systems (e.g., web search engines). Passive sensors, such as wearables, capturing physiological and behavioral data, have the potential to provide solutions in this context. This paper presents a novel dataset, SenseSeek, designed to evaluate the effectiveness of consumer-grade sensors in a complex information processing scenario: searching via systems (e.g., search engines), one of the common strategies users employ for information seeking. The SenseSeek dataset comprises data collected from 20 participants, 235 trials of the stimulated search process, 940 phases of stages in the search…
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