Detecting changes in content and posting time distributions in social media
Detecting changes in content and posting time distributions in social media
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DOI：
10.1145/2492517.2492618
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发表时间：
2013-08
期刊：
影响因子：
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通讯作者：
Kazumi Saito;K. Ohara;M. Kimura;H. Motoda
中科院分区：
文献类型：
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作者：
Kazumi Saito;K. Ohara;M. Kimura;H. Motoda
我们解决了检测发布到社交媒体的信息变化的问题，同时考虑到内容和发布时间分布。为此，我们引入了一种由两个组件组成的生成模型，一个用于内容分布，另一个用于时间分布，通过一系列阶跃函数来近似参数变化的形状。然后，我们提出了一种有效的算法，通过最大化生成观察序列数据的可能性来检测变化点，其时间复杂度几乎与观察序列（可能的变化点）的长度成正比。我们通过实验评估了合成数据流上的方法，并证明了考虑两种分布以提高准确性的重要性。我们进一步将我们的方法应用于从日本化妆品口碑传播网站提取的真实评分流数据，并表明它可以检测变化点，并且通过对实际评论的深入调查可以解释检测到的参数变化模式。
We address a problem of detecting changes in information posted to social media taking both content and posting time distributions into account. To this end, we introduce a generative model consisting of two components, one for a content distribution and the other for a timing distribution, approximating the shape of the parameter change by a series of step functions. We then propose an efficient algorithm to detect change points by maximizing the likelihood of generating the observed sequence data, which has time complexity almost proportional to the length of observed sequence (possible change points). We experimentally evaluate the method on synthetic data streams and demonstrate the importance of considering both distributions to improve the accuracy. We, further, apply our method to real scoring stream data extracted from a Japanese word-of-mouth communication site for cosmetics and show that it can detect change points and the detected parameter change patterns are interpretable through an in-depth investigation of actual reviews.