Networks of Audience Overlap in the Consumption of Digital News

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Networks of Audience Overlap in the Consumption of Digital News


文章共有三位作者,其中Subhayan Mukerjee和Sandra González-Bailón来自UPenn的Annenberg School for Communication,Sílvia Majó-Vázquez来自Oxford的Reuters Institute for the Study of Journalism。

后者所在的机构在2017年也发表过一篇Journal of Communication的文章 (Fletcher, 2017),也谈的是Audience Fragmentation和Audience Duplication的问题,用的方法大致还是Webster(2012)文章中的方法,即构建一个受众重叠(Audience Duplication)的网络,不过采用的数据比较新颖,用的是欧洲6个国家不同媒体(包括纸媒、电视媒体和互联网媒体)的用户数据,得到的结论是:1、受众重叠现象较普遍,不过在丹麦和英国比西班牙和美国有更高的分化程度。2、没有证据支撑线上的观众比线下的观众更碎片化,这有助于打消人们对“过滤泡泡” (Pariser, 2011) 的疑虑。


We propose two crucial improvements to the methodology employed in previous research: a statistical test to filter out non-significant overlap between sites; and a thresholding approach to identify the core of the audience network.

本文将Webster的筛选边的办法改进为,引入一个phi coefficient,定义为:

\phi =\frac{D_{ij}N-A_iA_j}{\sqrt{A_iA_j(N-A_i)(N-A_j)}}

This coefficient is positive when the overlap is larger than expected by chance.

To determine the statistical significance of the phi correlations we make use of the t statistic, which is also a conventional tool to determine how likely it would be to observe the measured overlap if the null hypothesis of no overlap were true. The t value is calculated with the formula:


In our approach, overlapping ties with t values below the probability threshold p < .01 are eliminated as non-significant. The methodology we propose here eliminates overlapping ties that do not reach the significance level for a probability value p < .01.

The centralization of audience networks can be referred to (Freeman, 1979)


Here, we proposed a series of methodological improvements to how audience overlap networks were analyzed in the past.

First, we showed that the structure of audience networks changes substantially when insignificant ties are removed (prior research did not apply a test of significance).

Second, we showed that the strength of the overlap (also disregarded in previous research) is crucial to uncover the core-periphery structure of audience networks. The two networks we analyze exhibit a very cohesive core, with no evidence of fragmentation. This core contains a few digital-born news sources but it is fundamentally formed by legacy brands, which still stand (by far) as the main sources of news online.

Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. Penguin UK.

Webster, J. G., & Ksiazek, T. B. (2012). The Dynamics of Audience Fragmentation: Public Attention in an Age of Digital Media. Journal of Communication, 62(1), 39–56.

Fletcher, R., & Nielsen, R. K. (2017). Are News Audiences Increasingly Fragmented? A Cross‐National Comparative Analysis of Cross‐Platform News Audience Fragmentation and Duplication. Journal of Communication.