When Attention Becomes Exposure in Generative Search

2026-01-05

arXiv.org

AI Summary

This study investigates whether citation exposure in generative search engines is influenced by external attention markets, thereby favoring already prominent voices. Through an audit of generative search results for 44 Web3 firms—combined with large-scale search log analysis, longitudinal tracking of creator community dynamics, and statistical association modeling—the work reveals, for the first time, a systematic exposure bias toward highly visible creators in these systems’ citation mechanisms. The findings demonstrate that popular creators receive significantly higher citation exposure, and that a firm’s follower count and creator concentration positively predict its exposure ranking. These results confirm that viewpoint diversity may be compromised by “exposure entrenchment,” providing empirical evidence to inform fairness-aware design principles for generative search systems.

Technology Category

• Search and Optimization: Other Foundations of Search & Optimization
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Generative search engines are reshaping information access by replacing traditional ranked lists with synthesized answers and references. In parallel, with the growth of Web3 platforms, incentive-driven creator ecosystems have become an essential part of how enterprises build visibility and community by rewarding creators for contributing to shared narratives. However, the extent to which exposure in generative search engine citations is shaped by external attention markets remains uncertain. In this study, we audit the exposure for 44 Web3 enterprises. First, we show that the creator community around each enterprise is persistent over time. Second, enterprise-specific queries reveal that more popular voices systematically receive greater citation exposure than others. Third, we find that larger follower bases and enterprises with more concentrated creator cores are associated with higher-ranked exposure. Together, these results show that generative search engine citations exhibit exposure bias toward already prominent voices, which risks entrenching incumbents and narrowing viewpoint diversity.

Problem

• generative search
• exposure bias
• attention markets
• Web3
• citation diversity

Innovation

• generative search
• exposure bias
• attention markets
• Web3
• citation dynamics