Beyond Topicality: A Conceptual Analysis of Societal Relevance and Its Application to Search Results and AI Responses
- URL: http://arxiv.org/abs/2607.09264v1
- Date: Fri, 10 Jul 2026 10:29:57 GMT
- Title: Beyond Topicality: A Conceptual Analysis of Societal Relevance and Its Application to Search Results and AI Responses
- Abstract summary: "Societal relevance" is a concept introduced by Haider and Sundin to address the limitations of traditional relevance models in web search.<n>This study investigates three analytical questions: the definition of societal relevance, its practical application in search systems, and its distinction from information quality measures.
- Score: 5.068057998312654
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- Analytical Search [27.61450958002869]
analytical search reframes search as an evidence-governed, process-oriented analytical workflow.<n>We present a unified system framework that integrates query understanding, recall-oriented retrieval, reasoning-aware fusion, and adaptive verification.
arXiv Detail & Related papers (2026-02-12T05:06:29Z) - Navigating the Shift: A Comparative Analysis of Web Search and Generative AI Response Generation [13.959899706228176]
The rise of generative AI as a primary information source presents a paradigm shift from traditional web search.<n>This paper presents a large-scale empirical study quantifying the fundamental differences between the results returned by Google Search and leading generative AI services.
arXiv Detail & Related papers (2026-01-23T16:06:18Z) - SciNetBench: A Relation-Aware Benchmark for Scientific Literature Retrieval Agents [12.057215000080705]
We propose SciNetBench, the first Scientific Network Relation-aware Benchmark for literature retrieval agents.<n>Our benchmark systematically evaluates three levels of relations: ego-centric retrieval of papers with novel knowledge structures, pair-wise identification of scholarly relationships, and path-wise reconstruction of scientific evolutionary trajectories.<n>We find that their accuracy on relation-aware retrieval tasks often falls below 20%, revealing a core shortcoming of current retrieval paradigms.
arXiv Detail & Related papers (2025-12-16T02:53:02Z) - Towards Context-aware Reasoning-enhanced Generative Searching in E-commerce [61.03081096959132]
We propose a context-aware reasoning-enhanced generative search framework for better textbfunderstanding the complicated context.<n>Our approach achieves superior performance compared with strong baselines, validating its effectiveness for search-based recommendation.
arXiv Detail & Related papers (2025-10-19T16:46:11Z) - From Web Search towards Agentic Deep Research: Incentivizing Search with Reasoning Agents [96.65646344634524]
Large Language Models (LLMs), endowed with reasoning and agentic capabilities, are ushering in a new paradigm termed Agentic Deep Research.<n>We trace the evolution from static web search to interactive, agent-based systems that plan, explore, and learn.<n>We demonstrate that Agentic Deep Research not only significantly outperforms existing approaches, but is also poised to become the dominant paradigm for future information seeking.
arXiv Detail & Related papers (2025-06-23T17:27:19Z) - Ethical Aspects of the Use of Social Robots in Elderly Care -- A Systematic Qualitative Review [0.0]
The use of social robotics in elderly care is increasingly discussed as one way of meeting emerging care needs due to scarce resources.<n>This review develops an overview on ethical aspects of the use of social robots in elderly care from a decision-makers' perspective.
arXiv Detail & Related papers (2025-05-14T08:46:24Z) - Toward Agentic AI: Generative Information Retrieval Inspired Intelligent Communications and Networking [87.82985288731489]
Agentic AI has emerged as a key paradigm for intelligent communications and networking.<n>This article emphasizes the role of knowledge acquisition, processing, and retrieval in agentic AI for telecom systems.
arXiv Detail & Related papers (2025-02-24T06:02:25Z) - The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning [70.16523526957162]
Understanding commonsense causality helps people understand the principles of the real world better.
Despite its significance, a systematic exploration of this topic is notably lacking.
Our work aims to provide a systematic overview, update scholars on recent advancements, and provide a pragmatic guide for beginners.
arXiv Detail & Related papers (2024-06-27T16:30:50Z) - Integrity and Junkiness Failure Handling for Embedding-based Retrieval:
A Case Study in Social Network Search [26.705196461992845]
Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc.
In this paper, we conduct an analysis of embedding-based retrieval launched in early 2021 on our social network search engine.
We define two main categories of failures introduced by it, integrity and junkiness.
arXiv Detail & Related papers (2023-04-18T20:53:47Z) - Causal Fairness Analysis [68.12191782657437]
We introduce a framework for understanding, modeling, and possibly solving issues of fairness in decision-making settings.
The main insight of our approach will be to link the quantification of the disparities present on the observed data with the underlying, and often unobserved, collection of causal mechanisms.
Our effort culminates in the Fairness Map, which is the first systematic attempt to organize and explain the relationship between different criteria found in the literature.
arXiv Detail & Related papers (2022-07-23T01:06:34Z) - Fairness in Recommender Systems: Research Landscape and Future
Directions [119.67643184567623]
We review the concepts and notions of fairness that were put forward in the area in the recent past.
We present an overview of how research in this field is currently operationalized.
Overall, our analysis of recent works points to certain research gaps.
arXiv Detail & Related papers (2022-05-23T08:34:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.