The Pulse of News in Social Media: Forecasting Popularity
The Pulse of News in Social Media: Forecasting Popularity
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DOI：
10.1609/icwsm.v6i1.14261
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发表时间：
2012-02
期刊：
影响因子：
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通讯作者：
Roja Bandari;S. Asur;B. Huberman
中科院分区：
文献类型：
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作者：
Roja Bandari;S. Asur;B. Huberman
新闻报道的时效性很强。新闻项目之间也存在着尽可能广泛传播的激烈竞争。因此，预测社交网络上新闻项目的受欢迎程度的任务既有趣又具有挑战性。先前的研究已经处理了基于早期流行度来预测最终的在线流行度。然而，最理想的是在发布之前预测项目的受欢迎程度，从而促进做出适当决策以修改文章及其发布方式的可能性。在本文中，我们构建了一个多维的特征空间，来自一篇文章的属性，并评估这些功能的有效性，作为在线流行度的预测。我们研究了回归和分类算法，并证明了尽管人类行为具有随机性，但预测twitter上的受欢迎程度范围是可能的，总体准确率为84%。我们的研究还有助于说明传统上突出的来源和社交网络上非常流行的来源之间的差异。
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.