Many attention markets exhibit stable patterns of concentration, where a few producers attract and sustain a far greater share of the audience than others. This inequality often follows patterns consistent with cumulative advantage, a process in which performance compounds over time. Attention to news sources online possesses these characteristics; however, online audiences also fragment across many disparate news producers. How do social media and recommender systems contribute to these attention dynamics? In this study, we examine two paradigmatic models: concentration driven by cumulative advantage and fragmentation driven by stochasticity. We evaluate these models against a large-scale empirical dataset of news source attention in the popular social media site Reddit. While we find high levels of attention concentration, we do not find the stable popularity over time that characterizes cumulative advantage. Rather, sources gain and lose popularity seemingly at random, aligning with a stochastic model. These results demonstrate the persistence of attention inequality, even in the absence of a strong driving mechanism. They also suggest that social media systems can undermine the accumulation of attention to the most prominent news sources. Digital attention markets striving for more equitable allocation require novel mechanisms of organizing and distributing information.

中文翻译：

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没有累积优势的集中：网络社区中新闻源关注度的分布

许多注意力市场表现出稳定的集中模式，其中少数制片人比其他制片人吸引并维持了更大的观众份额。这种不平等通常遵循与累积优势相一致的模式，在这个过程中，绩效会随着时间的推移而复杂化。对网络新闻来源的关注具有这些特点；然而，在线观众也分散在许多不同的新闻制作人中。社交媒体和推荐系统如何促成这些注意力动态？在这项研究中，我们研究了两个范式模型：由累积优势驱动的集中度和由随机性驱动的碎片化。我们将这些模型与流行的社交媒体网站 Reddit 中新闻来源关注的大规模经验数据集进行评估。虽然我们发现注意力高度集中，我们没有发现随着时间的推移稳定的流行度是累积优势的特征。相反，来源的受欢迎程度似乎是随机的，与随机模型一致。这些结果表明，即使在没有强有力的驱动机制的情况下，注意力不平等仍然存在。他们还表明，社交媒体系统可能会破坏对最重要新闻来源的关注度积累。追求更公平分配的数字注意力市场需要新的信息组织和分发机制。他们还表明，社交媒体系统可能会破坏对最重要新闻来源的关注度积累。追求更公平分配的数字注意力市场需要新的信息组织和分发机制。他们还表明，社交媒体系统可能会破坏对最重要新闻来源的关注度积累。追求更公平分配的数字注意力市场需要新的信息组织和分发机制。