Lifecycle Modeling for Buzz Temporal Pattern Discovery
Lifecycle Modeling for Buzz Temporal Pattern Discovery
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DOI：
10.1145/2994605
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发表时间：
2016-12
期刊：
影响因子：
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通讯作者：
Yi Chang;M. Yamada;Antonio Ortega;Yan Liu
中科院分区：
文献类型：
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作者：
Yi Chang;M. Yamada;Antonio Ortega;Yan Liu
在社交媒体分析中，一项关键任务是检测话题或嗡嗡声的爆发，这反映在短时间内对某些关键词的频繁提及。检测嗡嗡声不仅提供了有用的见解的信息传播机制，但也发挥了重要作用，防止恶意谣言。然而，嗡嗡声建模是一项具有挑战性的任务，因为嗡嗡声时间序列往往表现出突然的尖峰和沉重的尾巴，其中大多数现有的时间序列模型失败。在这篇文章中，我们提出了新的嗡嗡声建模方法，捕捉上升和衰落的时间模式，通过产品销售（PLC）模型，在经济学中的经典概念。更具体地说，我们建议在嗡嗡声时间序列与PLC混合物或PLC组混合物的多个峰值建模，并开发一个概率图形模型（K-MPLC产品混合物）自动发现固有的生命周期模式内的嗡嗡声集合。此外，我们有效地利用PLC混合物或PLC基团混合物的模型参数进行突发预测。我们的实验结果表明，我们提出的方法显着优于现有的领先的嗡嗡声聚类和嗡嗡声类型的预测方法。
In social media analysis, one critical task is detecting a burst of topics or buzz, which is reflected by extremely frequent mentions of certain keywords in a short-time interval. Detecting buzz not only provides useful insights into the information propagation mechanism, but also plays an essential role in preventing malicious rumors. However, buzz modeling is a challenging task because a buzz time-series often exhibits sudden spikes and heavy tails, wherein most existing time-series models fail. In this article, we propose novel buzz modeling approaches that capture the rise and fade temporal patterns via Product Lifecycle (PLC) model, a classical concept in economics. More specifically, we propose to model multiple peaks in buzz time-series with PLC mixture or PLC group mixture and develop a probabilistic graphical model (K-Mixture of Product Lifecycle (K-MPLC) to automatically discover inherent lifecycle patterns within a collection of buzzes. Furthermore, we effectively utilize the model parameters of PLC mixture or PLC group mixture for burst prediction. Our experimental results show that our proposed methods significantly outperform existing leading approaches on buzz clustering and buzz-type prediction.