Modest Algorithmic Mediation can Maximize Topical Diversity in Hybrid Human-AI Systems

2026-07-27

AI Summary

This study investigates how algorithmic recommendation and social network diffusion jointly shape the thematic diversity of information sharing and inequality among users. By developing a hybrid human–algorithm information diffusion model that integrates both mechanisms, and leveraging large-scale user behavior data, parameter calibration, and simulation experiments, the research uncovers a non-monotonic impact of algorithmic intervention on diversity and equity. The findings reveal that moderate algorithmic involvement—such as an intervention proportion of approximately 0.50 observed post-2016—can simultaneously enhance average thematic diversity and reduce inequality, challenging the prevailing assumption that algorithms inevitably narrow users’ informational horizons. These insights offer both theoretical grounding and practical pathways for optimizing the information ecosystem.

Abstract

In the artificial intelligence (AI) era, the rise of algorithmic feeds has fundamentally transformed information diffusion on social media. While early platforms organized visibility through explicit social networks, contemporary systems mediate exposure through intelligent recommender algorithms that personalize attention. This paper examines how the social network and algorithmic architecture jointly shape the diversity of information sharing. Analysis of 18,076 users active throughout 2014--2018 shows that the topical diversity of sharing rose and then plateaued after the introduction of algorithmic ranking in 2016 while its inequality across users emerged alongside it. To this end, we introduce a hybrid human-AI information diffusion model in which information exposure is governed by a parameterized mixture of social propagation through the user-following network and algorithmic recommendation. Both qualitative analysis and simulations show that the effect of algorithmic mediation is non-monotonic. Modest mediation can raise average diversity and reduce inequality relative to a purely network-driven baseline, whereas strong mediation reduces diversity and concentrates it among fewer users. Fitting the model to four years of data yields a mediation share that increases from zero before 2016 to approximately 0.50 by 2018, a level that exceeds the compensation point of equality while remaining within the diversity-enhancing range. These results identify the conditions under which recommendation broadens rather than narrows exposure and provide a unified framework for information diffusion in hybrid human-AI systems.

Problem

• topical diversity
• algorithmic mediation
• information diffusion
• social media
• hybrid human-AI systems

Innovation

• algorithmic mediation
• topical diversity
• hybrid human-AI systems
• information diffusion
• recommender systems