AI 中文总结
研究社交网络与算法架构对信息共享多样性的影响，引入混合人机信息传播模型，发现适度算法调解可提升平均多样性、减少不平等，强调解则相反，还确定了推荐扩大曝光的条件并提供统一框架。
AI 中文摘要
在人工智能时代，算法推送的兴起从根本上改变了社交媒体上的信息传播。早期平台通过明确的社交网络组织可见性，当代系统则通过智能推荐算法进行曝光调解。本文研究社交网络和算法架构如何共同塑造信息共享的多样性。对2014年至2018年活跃的18076名用户的分析表明，2016年引入算法排名后，共享的主题多样性先上升后趋于平稳，同时用户间的不平等也随之出现。为此，引入了一个混合人机信息传播模型，信息曝光由通过用户关注网络的社会传播和算法推荐的参数化混合来控制。定性分析和模拟表明，算法调解的效果是非单调的。适度调解相对于纯网络驱动的基线可以提高平均多样性并减少不平等，而强调解会降低多样性并将其集中在较少用户中。将模型拟合到四年的数据中，得到的调解份额从2016年前的零增加到2018年的约0.50，这一水平超过了平等补偿点，同时仍在增强多样性的范围内。这些结果确定了推荐扩大而非缩小曝光的条件，并为混合人机系统中的信息传播提供了一个统一框架。
英文摘要
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.
