AI 中文总结
研究针对传统网络搜索相关性模型局限提出社会相关性概念，通过分析相关问题及多种相关性组合，探讨优化搜索输出以实现“更大利益”，为开发注重道德和社会利益的价值驱动搜索引擎提供框架。
AI 中文摘要
本文探讨了由海德尔和桑丁引入的“社会相关性”概念，以解决网络搜索中传统相关性模型的局限性。虽然主题相关性和用户相关性是信息科学的基础，但不足以管理不受控制的网络上的有害内容。本研究调查了三个分析问题：社会相关性的定义、其在搜索系统中的实际应用以及与信息质量度量的区别。通过分析系统、用户和社会相关性的各种组合，探讨如何为“更大的利益”优化搜索输出。尽管该概念在理论上仍未充分发展，但为开发价值驱动的搜索引擎提供了重要框架，该引擎将道德结果和社会利益置于单纯的关键词匹配之上。
英文摘要
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.
