ISMIE: A Framework to Characterize Information Seeking in Modern Information Environments

2025-10-08

AI Summary

Contemporary information environments (MIEs) have grown increasingly complex, rendering existing information-seeking (IS) models inadequate for addressing emergent challenges—such as eroded trust in AI-generated content and dopamine-driven consumption behaviors. Method: This paper introduces the ISMIE framework, the first to integrate dual user- and system-centric perspectives, modeling MIE-based information seeking through three core elements: components, intervening variables, and activities. Drawing on conceptual modeling and six case studies of retrieval models under misinformation propagation scenarios, the analysis exposes structural limitations of prevailing IS models and identifies three urgent research gaps. Contribution/Results: The paper proposes two scalable, actionable research design blueprints. ISMIE establishes a novel paradigm that bridges theoretical rigor and practical applicability for modeling information behavior, optimizing information systems, and conducting empirical research in MIEs.

Technology Category

• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The modern information environment (MIE) is increasingly complex, shaped by a wide range of techniques designed to satisfy users' information needs. Information seeking (IS) models are effective mechanisms for characterizing user-system interactions. However, conceptualizing a model that fully captures the MIE landscape poses a challenge. We argue: Does such a model exist? To address this, we propose the Information Seeking in Modern Information Environments (ISMIE) framework as a fundamental step. ISMIE conceptualizes the information seeking process (ISP) via three key concepts: Components (e.g., Information Seeker), Intervening Variables (e.g., Interactive Variables), and Activities (e.g., Acquiring). Using ISMIE's concepts and employing a case study based on a common scenario - misinformation dissemination - we analyze six existing IS and information retrieval (IR) models to illustrate their limitations and the necessity of ISMIE. We then show how ISMIE serves as an actionable framework for both characterization and experimental design. We characterize three pressing issues and then outline two research blueprints: a user-centric, industry-driven experimental design for the authenticity and trust crisis to AI-generated content and a system-oriented, academic-driven design for tackling dopamine-driven content consumption. Our framework offers a foundation for developing IS and IR models to advance knowledge on understanding human interactions and system design in MIEs.

Problem

• Characterizing information seeking in complex modern environments
• Addressing limitations of existing information retrieval models
• Providing framework for human-system interaction analysis

Innovation

• Proposes ISMIE framework for modeling information seeking
• Characterizes process via components, variables, and activities
• Provides foundation for experimental design in information environments