ABSTRACT Although communication scholars have increasingly studied audience fragmentation by analyzing audience networks, recent cross-national research has spurred a methodological debate about how to construct these audience networks from media use data. One point of contention lies in the choice of filtering procedures that only keep meaningful ties, where the traditional deviation-from-random-duplication filter has been replaced by alternatives such as backbone extraction. Using survey data from the 2016 Reuters Institute Digital News Report on news usage in 26 countries, we examined how filtering choices impact the structures of audience networks through a resampling simulation experiment. The simulation revealed a strong divergence between the filtering approaches, which invited disparate interpretations regarding the fragmentation of modern news audiences. The backbone extraction approach produced the most distinct results, while dealing most effectively with randomness in the media use data. As the filtering approaches mapped the structural features of audiences to different values of the same network metrics, the findings imply that studies diverging in these analytical decisions do not produce compatible results.

中文翻译：

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过滤选择如何影响受众网络的结构？使用来自26个国家/地区的数据进行的模拟研究

摘要尽管传播学者越来越多地通过分析受众网络来研究受众细分，但最近的跨国研究引发了关于如何从媒体使用数据构建这些受众网络的方法论辩论。争论的焦点之一是选择仅保持有意义联系的过滤程序，在这种方法中，传统的“偏离随机复制”过滤器已被诸如骨干提取之类的替代方法所取代。我们使用2016年《路透社数字新闻报道》中有关26个国家新闻使用情况的调查数据，通过重新采样模拟实验研究了过滤选择如何影响受众网络的结构。仿真显示，过滤方法之间存在很大差异，这就引起了关于现代新闻受众分散的不同解释。骨干提取方法产生了最明显的结果，同时最有效地处理了媒体使用数据中的随机性。由于过滤方法将受众的结构特征映射到同一网络指标的不同值，因此发现暗示在这些分析决策中进行分歧的研究不会产生兼容的结果。