基于分层Dirichlet过程的Twitter子故事检测
Sub-Story Detection in Twitter with Hierarchical Dirichlet Processes
摘要：
社交媒体现在已经成为现实世界事件的事实上的信息源。然而，由于社交媒体流的高容量和速度性质，挑战在于如何随着时间的推移跟踪与给定事件有关的所有帖子，这是一项称为故事检测的任务。此外，通常有几个不同的故事属于一个给定的事件，我们称之为子故事和相应的任务，他们的自动检测子故事检测。本文提出了层次狄利克雷过程（HDP），概率主题模型，作为一种有效的方法自动子故事检测。HDP可以学习与子故事相关联的子主题，这使得它能够处理子故事中的微妙变化。它是比较国家的最先进的故事检测方法的基础上局部敏感哈希和谱聚类。我们展示了优越的性能HDP子故事检测真实世界的Twitter数据集，使用各种评估措施。HDP学习子主题的能力有助于它以高精度回忆子故事。本文的另一个贡献是证明了Twitter流中的会话结构可以用来显着提高子故事检测性能。
英文摘要：
Social media has now become the de facto information source on real world events. The challenge, however, due to the high volume and velocity nature of social media streams, is in how to follow all posts pertaining to a given event over time, a task referred to as story detection. Moreover, there are often several different stories pertaining to a given event, which we refer to as sub-stories and the corresponding task of their automatic detection as sub-story detection. This paper proposes hierarchical Dirichlet processes (HDP), a probabilistic topic model, as an effective method for automatic sub-story detection. HDP can learn sub-topics associated with sub-stories which enables it to handle subtle variations in sub-stories. It is compared with state- of-the-art story detection approaches based on locality sensitive hashing and spectral clustering. We demonstrate the superior performance of HDP for sub-story detection on real world Twitter data sets using various evaluation measures. The ability of HDP to learn sub-topics helps it to recall the sub- stories with high precision. Another contribution of this paper is in demonstrating that the conversational structures within the Twitter stream can be used to improve sub-story detection performance significantly.
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。
