提出社会相关性概念，让搜索更关注公共利益而非仅关键词匹配
Beyond Topicality: A Conceptual Analysis of Societal Relevance and Its Application to Search Results and AI Responses
- 引入社会相关性概念，弥补传统搜索忽略有害内容的缺陷
- 通过三重相关性分析，探索搜索结果如何服务社会整体利益
- 适合关注AI伦理与搜索公平性的研究者和产品设计者
本文探讨了由Haider和Sundin提出的“社会相关性”概念，旨在解决传统相关性模型在网页搜索中的局限。尽管主题相关性和用户相关性是信息科学的基础，但它们不足以应对开放网络中广泛存在的虚假信息或歧视性内容等有害内容。本研究围绕三个核心问题展开：社会相关性的定义、其在搜索系统中的实际应用，以及它与信息质量评估的区别。通过分析系统、用户与社会相关性的多种组合，论文探索如何使搜索输出更有利于“公共福祉”。尽管该概念尚处于理论初步阶段，但它为构建以价值观为导向、优先考虑伦理结果与社会利益的搜索引擎提供了关键框架。
原文摘要 · Abstract (English)
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.
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