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Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia

机译：信息网络中的内容增长和注意力传递：涉及维基百科的信息贫困

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Open collaboration platforms have fundamentally changed the way that knowledge is produced, disseminated, and consumed. In these systems, contributions arise organically with little to no central governance. Although such decentralization provides many benefits, a lack of broad oversight and coordination can leave questions of information poverty and skewness to the mercy of the system's natural dynamics. Unfortunately, we still lack a basic understanding of the dynamics at play in these systems and specifically, how contribution and attention interact and propagate through information networks. We leverage a large-scale natural experiment to study how exogenous content contributions to Wikipedia articles affect the attention that they attract and how that attention spills over to other articles in the network. Results reveal that exogenously added content leads to significant, substantial, and long-term increases in both content consumption and subsequent contributions. Furthermore, we find significant attention spillover to downstream hyperlinked articles. Through both analytical estimation and empirically informed simulation, we evaluate policies to harness this attention contagion to address the problem of information poverty and skewness. We find that harnessing attention contagion can lead to as much as a twofold increase in the total attention flow to clusters of disadvantaged articles. Our findings have important policy implications for open collaboration platforms and information networks.
机译：开放协作平台从根本上改变了所产生，传播和消费的知识的方式。在这些系统中，贡献有机地出现，几乎没有中枢治理。虽然这种权力下放提供了许多好处，但缺乏广泛的监督和协调可以将信息贫困和偏离的问题留给系统自然动态的怜悯。不幸的是，我们仍然缺乏对这些系统中发挥的动态的基本了解，具体而言，如何通过信息网络互动和传播的贡献和传播。我们利用大规模的自然实验来研究对维基百科文章的外源内容贡献如何影响他们吸引的注意力以及将注意力溢出到网络中的其他文章。结果表明，外源添加的含量导致内容消费和随后的贡献中的显着，大幅度和长期增加。此外，我们发现对下游超链接物品的重视溢出。通过分析估计和经验知情的模拟，我们评估了利用这种关注蔓延的政策来解决信息贫困和歪曲问题。我们发现利用关注传染率可能导致两种弱势文章集群的两倍增加。我们的调查结果对开放协作平台和信息网络具有重要的政策影响。

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