In this paper we seek to understand the relationship between the online
popularity of an article and its intrinsic quality. Prior experimental work
suggests that the relationship between quality and popularity can be very
distorted due to factors like social influence bias and inequality in
visibility. We conduct a study of popularity on two different social news
aggregators, Reddit and Hacker News. We define quality as the relative number
of votes an article would have received if each article was shown, in a
bias-free way, to an equal number of users. We propose a simple poisson
regression method to estimate this quality metric from time-series voting data.
We validate our methods on data from Reddit and Hacker News, as well the
experimental data from prior work. This method works well even though the
collected data is subject to common social media biases. Using these estimates,
we find that popularity on Reddit and Hacker News is a stronger reflection of
intrinsic quality than expected.

本文旨在探究文章在线受欢迎程度与其内在质量的关系，提出了一种基于泊松回归模型的质量估计方法，并通过 Reddit 和 Hacker News 平台的数据验证了该方法的有效性。结果表明，这种方法能够有效消除社交媒体的偏见，从而发现文章的受欢迎程度更加反应其真正的内在质量。