论文标题
社交网络中的回声室和隔离：马尔可夫桥模型和估计
Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation
论文作者
论文摘要
本文介绍了社会网络中称为Echo Chambers的社会学现象的建模和估计。具体而言，我们提出了一种基于社区的新型图形模型，该模型代表了隔离的回声室作为马尔可夫桥工艺的出现。马尔可夫桥（Markov Bridge）是一个一维的马尔可夫随机领域，可在确定性时期建模社区的形成和分离，这在具有已知定时事件的社交网络中很重要。我们通过六个现实世界示例证明了提出的模型合理，并在最近的Twitter数据集上检查了其性能。我们根据最大似然和使用从网络获得的嘈杂样本进行递归估算隔离水平的贝叶斯过滤算法提供模型参数估计算法。数值结果表明，所提出的过滤算法在均值误差方面优于常规隐藏的马尔可夫建模。所提出的过滤方法在计算社会科学中很有用，在计算社会科学中，需要从嘈杂数据中数据驱动的隔离水平进行估计。
This paper deals with the modeling and estimation of the sociological phenomena called echo chambers and segregation in social networks. Specifically, we present a novel community-based graph model that represents the emergence of segregated echo chambers as a Markov bridge process. A Markov bridge is a one-dimensional Markov random field that facilitates modeling the formation and disassociation of communities at deterministic times which is important in social networks with known timed events. We justify the proposed model with six real world examples and examine its performance on a recent Twitter dataset. We provide model parameter estimation algorithm based on maximum likelihood and, a Bayesian filtering algorithm for recursively estimating the level of segregation using noisy samples obtained from the network. Numerical results indicate that the proposed filtering algorithm outperforms the conventional hidden Markov modeling in terms of the mean-squared error. The proposed filtering method is useful in computational social science where data-driven estimation of the level of segregation from noisy data is required.
