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。

社交新闻聚合器中的人气与质量：对 Reddit 和 Hacker News 的研究

Popularity and Quality in Social News Aggregators: A Study of Reddit and  Hacker News

News articles are extremely time sensitive by nature. There is also intense
competition among news items to propagate as widely as possible. Hence, the
task of predicting the popularity of news items on the social web is both
interesting and challenging. Prior research has dealt with predicting eventual
online popularity based on early popularity. It is most desirable, however, to
predict the popularity of items prior to their release, fostering the
possibility of appropriate decision making to modify an article and the manner
of its publication. In this paper, we construct a multi-dimensional feature
space derived from properties of an article and evaluate the efficacy of these
features to serve as predictors of online popularity. We examine both
regression and classification algorithms and demonstrate that despite
randomness in human behavior, it is possible to predict ranges of popularity on
twitter with an overall 84% accuracy. Our study also serves to illustrate the
differences between traditionally prominent sources and those immensely popular
on the social web.

本文旨在通过建立多维特征空间来分析新闻文章的性质，采用回归和分类算法预测其在线受欢迎程度，发现可在 Twitter 上以 84% 的准确率范围内预测其受欢迎度，并突显传统来源与社交网络受欢迎性的差异。