Identifying exogenous and endogenous activity in social media
Identifying exogenous and endogenous activity in social media
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DOI：
10.1103/physreve.98.052304
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发表时间：
2018-11-13
影响因子：
2.4
通讯作者：
Shinomoto, Shigeru
中科院分区：
文献类型：
--
作者：
Fujita, Kazuki;Medvedev, Alexey;Shinomoto, Shigeru
系统中新事件的发生通常是由外部原因和系统内部发生的先前事件驱动的。这是一个一般性的陈述，适用于一系列情况，包括最近的在线社交网络 (OSN) 中的用户活动。在这里，我们开发了一种方法，用于从一系列发布时间中提取外源性（例如新闻媒体）和内源性（例如信息级联）的相对贡献。该方法基于配备自激机制的广义线性模型（GLM）的拟合。我们使用非线性霍克斯过程生成的合成数据来测试该方法，并将其应用于具有给定主题标签的实时推文序列。在经验数据集中，外源和内源量的估计贡献分别接近原始推文和转发的数量。最后，我们讨论了该方法的可能应用，例如在线营销。
The occurrence of new events in a system is typically driven by external causes and by previous events taking place inside the system. This is a general statement, applying to a range of situations including, more recently, to the activity of users in online social networks (OSNs). Here we develop a method for extracting from a series of posting times the relative contributions that are exogenous, e.g., news media, and endogenous, e.g., information cascade. The method is based on the fitting of a generalized linear model (GLM) equipped with a self-excitation mechanism. We test the method with synthetic data generated by a nonlinear Hawkes process, and apply it to a real time series of tweets with a given hashtag. In the empirical dataset, the estimated contributions of exogenous and endogenous volumes are close to the amounts of original tweets and retweets respectively. We conclude by discussing the possible applications of the method, for instance in online marketing.