Early detection of persistent topics in social networks.
Early detection of persistent topics in social networks.
复制标题
及早发现社交网络中的持久话题。
DOI：
10.1007/s13278-015-0257-1
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发表时间：
2015
影响因子：
2.8
通讯作者：
and Kenji.Yamanish
中科院分区：
文献类型：
--
作者：
Shota .Saito;Ryota.Tomioka;and Kenji.Yamanish
在社交网络服务 (SNS) 中，持久性主题极其罕见且有价值。在本文中，我们提出了一种基于主题图的 SNS 中持久主题检测算法。主题图是普通社交网络图的子图，由在某个时间点共享某个主题的用户组成。基于与持久性和非持久性主题相关的主题图的时间演化不同的假设，我们提出通过对从主题图的时间演化中提取的特征值进行异常检测来检测持久性主题。对于异常检测，我们使用主成分分析来捕获正常（非持久）主题跨越的子空间。我们在从 Twitter 收集的真实数据集上展示了我们的技术，并表明它的性能明显优于基于幂律曲线拟合、线性影响模型、岭回归和支持向量机的基线方法。
In social networking services (SNSs), persistent topics are extremely rare and valuable. In this paper, we propose an algorithm for the detection of persistent topics in SNSs based on Topic Graph. A topic graph is a subgraph of the ordinary social network graph that consists of the users who shared a certain topic up to some time point. Based on the assumption that the time evolutions of the topic graphs associated with persistent and non-persistent topics are different, we propose to detect persistent topics by performing anomaly detection on the feature values extracted from the time evolution of the topic graph. For anomaly detection, we use principal component analysis to capture the subspace spanned by normal (non-persistent) topics. We demonstrate our technique on a real dataset we gathered from Twitter and show that it performs significantly better than a baseline method based on power-law curve fitting, the linear influence model, ridge regression, and Support Vector Machine.