Beyond Topicality: A Conceptual Analysis of Societal Relevance and Its Application to Search Results and AI Responses
For information science and search engine design, this paper offers a conceptual framework for value-driven search, though it is purely theoretical and lacks empirical validation.
This paper conceptually analyzes 'societal relevance' as an alternative to traditional relevance models in web search, aiming to address harmful content like misinformation. It explores how search outputs can be optimized for the 'greater good' but notes the concept remains theoretically underdeveloped.
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.