标题： SEISMIC：一种用于预测微博热度的自激发点过程模型 显示英文标题

标题： SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity

摘要： 社交网络网站允许用户创建和分享内容。这些网站的用户通过与朋友和关注者分享他人的帖子，可以形成大规模的信息级联。理解这种级联行为的核心挑战之一是预测信息爆发，其中一条帖子通过许多用户的分享变得广受欢迎。在本文中，我们专注于预测给定帖子的最终分享数量。我们基于自激发点过程理论，开发了一个统计模型，使我们能够做出准确的预测。我们的模型不需要训练或昂贵的特征工程。它产生了一个简单且可高效计算的公式，使我们能够实时回答诸如以下问题：鉴于帖子到目前为止的分享历史，我们对其最终分享数量的当前估计是多少？该帖子的分享级联是否已经过了爆炸性增长的初始阶段？以及，哪些帖子将来会被最多地分享？我们使用一个月的完整推特数据验证了我们的模型，并展示了比现有方法在预测准确性上的显著提升。在仅观察一个小时后，我们的模型在预测平均信息级联的最终规模时仅有15%的相对误差。 显示英文摘要

摘要： Social networking websites allow users to create and share content. Big information cascades of post resharing can form as users of these sites reshare others' posts with their friends and followers. One of the central challenges in understanding such cascading behaviors is in forecasting information outbreaks, where a single post becomes widely popular by being reshared by many users. In this paper, we focus on predicting the final number of reshares of a given post. We build on the theory of self-exciting point processes to develop a statistical model that allows us to make accurate predictions. Our model requires no training or expensive feature engineering. It results in a simple and efficiently computable formula that allows us to answer questions, in real-time, such as: Given a post's resharing history so far, what is our current estimate of its final number of reshares? Is the post resharing cascade past the initial stage of explosive growth? And, which posts will be the most reshared in the future? We validate our model using one month of complete Twitter data and demonstrate a strong improvement in predictive accuracy over existing approaches. Our model gives only 15% relative error in predicting final size of an average information cascade after observing it for just one hour.