Beyond Topicality: A Conceptual Analysis of Societal Relevance and Its Application to Search Results and AI Responses

2026-07-10

AI Summary

This work proposes the concept of “social relevance” to address the limitations of existing search relevance models, which primarily focus on topical matching and often fail to identify or mitigate harmful content such as misinformation and discriminatory material, thereby neglecting broader societal interests. Through conceptual analysis and a three-dimensional modeling framework encompassing system, user, and societal perspectives, the study clarifies the definition, boundaries, and distinction of social relevance from traditional information quality metrics. By transcending conventional relevance paradigms, this framework provides a theoretical foundation for designing retrieval systems that integrate ethical values and social welfare, ultimately advancing search engines toward a value-driven paradigm.

Abstract

This paper examines "societal relevance," a concept introduced by Haider and Sundin to address the limitations of traditional relevance models in web search. While topical and user relevance are foundational to information science, they are insufficient for managing harmful content such as misinformation or discrimination found on the uncontrolled web. This study investigates three analytical questions: the definition of societal relevance, its practical application in search systems, and its distinction from information quality measures. By analyzing various combinations of system, user, and societal relevance, the paper explores how search outputs can be optimized for the "greater good". Although the concept remains theoretically underdeveloped, it provides a vital framework for developing value-driven search engines that prioritize ethical outcomes and societal interests over mere keyword matching.

Problem

• societal relevance
• harmful content
• information retrieval
• ethical outcomes
• search systems

Innovation

• societal relevance
• search systems
• information ethics
• value-driven AI
• harmful content mitigation