双混合自激过程再共享级联在线项的描述与预测
Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes
摘要：
众所周知，在线行为是长尾的，大多数级联行为都很短，少数行为很长。在线事件生成模型的一个突出缺点是无法很好地描述不受欢迎的项目。这项工作解决了这些缺点，提出双混合物自激过程，共同学习组级联。我们首先从观察，内容病毒性和影响力衰减的最大似然估计是可分离的霍克斯过程。接下来，我们提出的模型，它利用了Borel混合模型和内核混合模型，联合建模的一组异构级联的展开。当应用于相同在线项目的级联时，该模型直接表征其传播动态并提供可解释的量，例如内容病毒式传播和内容影响力衰减，以及用于预测最终内容流行度的方法。在两个转发级联数据集上-一个与YouTube视频有关，第二个与有争议的新闻文章有关-我们展示了我们的模型在项目，发布商和类别的粒度上捕获在线项目之间的差异。特别是，我们能够区分极右翼，阴谋，有争议和有信誉的在线新闻文章，根据它们如何通过社交媒体传播，达到F1得分0.945。在holdout数据集上，我们证明了双混合模型对于重are扩散级联，特别是不受欢迎的扩散级联，提供了更好的泛化性能，并且对于在线项目，提供了准确的项目流行度预测。
英文摘要：
It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events is the inability to describe unpopular items well. This work addresses these shortcomings by proposing dual mixture self-exciting processes to jointly learn from groups of cascades. We first start from the observation that maximum likelihood estimates for content virality and influence decay are separable in a Hawkes process. Next, our proposed model, which leverages a Borel mixture model and a kernel mixture model, jointly models the unfolding of a heterogeneous set of cascades. When applied to cascades of the same online items, the model directly characterizes their spread dynamics and supplies interpretable quantities, such as content virality and content influence decay, as well as methods for predicting the final content popularities. On two retweet cascade datasets -- one relating to YouTube videos and the second relating to controversial news articles -- we show that our models capture the differences between online items at the granularity of items, publishers and categories. In particular, we are able to distinguish between far-right, conspiracy, controversial and reputable online news articles based on how they diffuse through social media, achieving an F1 score of 0.945. On holdout datasets, we show that the dual mixture model provides, for reshare diffusion cascades especially unpopular ones, better generalization performance and, for online items, accurate item popularity predictions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
