GUIDAETA - A Versatile Interactions Dataset with extensive Context Information and Metadata

2025-11-25

AI Summary

Existing interactive datasets suffer from limited scale, impoverished contextual information, and poor cross-domain applicability, hindering interdisciplinary research in cognitive science, human-computer interaction (HCI), and cybersecurity. To address these limitations, this work introduces the largest and most contextually rich publicly available guided-interaction dataset to date. We conducted controlled experiments using a custom-built consumer information system, collecting behavioral data from 250+ participants across 716 information retrieval tasks—comprising 2.39 million fine-grained interaction events, synchronized mouse/keyboard streams, system-level events, and rendered content. Concurrently, we integrated demographic attributes, real-time cognitive load assessments, and multidimensional usability feedback. This represents the first systematic integration of component-level interactions, system state, content presentation, and subjective metadata—significantly enhancing data reusability and cross-scenario generalizability.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements

Abstract

Interaction data is widely used in multiple domains such as cognitive science, visualization, human computer interaction, and cybersecurity, among others. Applications range from cognitive analyses over user/behavior modeling, adaptation, recommendations, to (user/bot) identification/verification. That is, research on these applications - in particular those relying on learned models - require copious amounts of structured data for both training and evaluation. Different application domains thereby impose different requirements. I.e., for some purposes it is vital that the data is based on a guided interaction process, meaning that monitored subjects pursued a given task, while other purposes require additional context information, such as widget interactions or metadata. Unfortunately, the amount of publicly available datasets is small and their respective applicability for specific purposes limited. We present GUIDEd Interaction DATA (GUIDAETA) - a new dataset, collected from a large-scale guided user study with more than 250 users, each working on three pre-defined information retrieval tasks using a custom-built consumer information system. Besides being larger than most comparable datasets - with 716 completed tasks, 2.39 million mouse and keyboard events (2.35 million and 40 thousand, respectively) and a total observation period of almost 50 hours - its interactions exhibit encompassing context information in the form of widget information, triggered (system) events and associated displayed content. Combined with extensive metadata such as sociodemographic user data and answers to explicit feedback questionnaires (regarding perceived usability, experienced cognitive load, pre-knowledge on the information system's topic), GUIDAETA constitutes a versatile dataset, applicable for various research domains and purposes.

Problem

• Public interaction datasets are scarce and limited for specific research applications
• Existing datasets lack comprehensive context information and extensive metadata
• There is insufficient structured interaction data for training and evaluating learned models

Innovation

• Large-scale guided user study with 250+ participants
• Custom-built consumer information system for data collection
• Comprehensive dataset with widget interactions and metadata