Online news can quickly reach and affect millions of people, yet we do not know yet whether there exist potential dynamical regularities that govern their impact on the public. We use data from two major news outlets, BBC and New York Times, where the number of user comments can be used as a proxy of news impact. We find that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems. In particular, we find that a simple exponential distribution yields a better fit to the empirical news impact distributions than a power-law distribution. This observation is explained by the lack or limited influence of the otherwise omnipresent rich-get-richer mechanism in the analyzed data. The temporal dynamics of the news impact exhibits a universal exponential decay which allows us to collapse individual news trajectories into an elementary single curve. We also show how daily variations of user activity directly influence the dynamics of the article impact. Our findings challenge the universal applicability of popularity dynamics patterns found in other social contexts.
翻译:在线新闻可以迅速传达到并影响数百万人, 但我们还不知道是否有潜在的动态规律来管理其对公众的影响。 我们使用英国广播公司和《纽约时报》这两个主要新闻机构的数据,其中用户的评论数量可以用作新闻影响的代理。 我们发现,在线新闻文章的影响动态并不显示许多其他社会和信息系统中发现的受欢迎模式。 特别是,我们发现简单的指数分布比权力法传播更适合实证新闻影响分布。 这一观察的解释是,在分析的数据中,缺乏或影响有限,否则就无处不在的富饶富饶型机制。 新闻影响的时空动态显示普遍指数衰变,使我们得以将个别新闻截图破碎成基本单一曲线。 我们还显示用户活动的日常变化如何直接影响文章影响动态。 我们的发现挑战了在其他社会环境中发现的普惠性动态模式的普遍适用性。