回关集群、卫星受众与桥接节点：2020年美国大选的共同参与网络
Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election
AI总结：
本文提出共同参与变换方法将大规模社交网络转化为可解释的可视化图像，并通过对2020年美国大选Twitter数据的案例研究，识别出桥接账号、卫星受众和回关社区等关键结构，同时发布开源代码。
摘要：
2020年美国总统大选一直是，并且持续是，通过我们的媒体生态系统（包括社交媒体）传播的普遍且持久的错误信息和虚假信息的焦点。这一事件推动了对大型有向社会网络数据集的收集和分析，但此类数据集可能难以被直观理解。在如此大规模的数据集中，典型表示中存在的数量庞大的节点和边会产生视觉伪影，例如密集重叠的边和低度节点的紧密堆积结构，这些伪影掩盖了许多更具实际意义的特征。我们应用一种称为共同参与变换的方法，将此类社交数据网络转换为易于处理的图像。直观地说，这种方法允许参数化的网络可视化，使参与观众的共享受众对观察者而言变得显著。利用该方法的可解释能力，我们对Twitter上的2020年美国总统大选进行了广泛的案例研究，贡献了对共同参与的实证分析。通过在不同参数集下创建和对比不同的网络，我们定义并刻画了该话语网络中的若干结构，包括桥接账号、卫星受众和回关社区。我们讨论了这些实证网络特征在此背景下的重要性和影响。此外，我们发布了用于从Twitter和其他结构化交互数据创建共同参与网络的开源代码。
英文摘要：
The 2020 United States presidential election was, and has continued to be, the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
