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Can Cascades be Predicted?

机译：可以预测级联吗？

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摘要

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest.
机译：在许多社交网站上，例如Facebook和Twitter，重新共享或重新发布功能允许用户与自己的朋友或关注者共享其他人的内容。在用户之间共享内容时，会形成大量的重新共享。尽管越来越多的研究致力于分析和表征此类级联，但最近的一项平行工作认为，级联的未来轨迹可能固有地不可预测。在这项工作中，我们开发了一个用于解决级联预测问题的框架。在Facebook上大量照片共享级联的样本中，我们发现在预测级联在将来是否会继续增长方面具有很强的性能。我们发现，级联的相对增长随着我们观察到更多的重复性而变得更加可预测，时间和结构特征是级联大小的关键预测器，并且最初，级联中的宽度而不是深度是更大的更好指示。级联。从多个不同类别的特征都实现相似性能的意义上来说，这种预测性能是可靠的。我们还发现时间特征可以预测级联的最终形状。观察相同内容的独立级联，我们发现尽管这些级联的大小差异很大，但我们仍然能够预测哪个最终最大。

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