Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation
Echo Chambers and Segregation in Social Networks: Markov Bridge Models and Estimation
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DOI：
10.1109/tcss.2021.3091168
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发表时间：
2020-12
影响因子：
5
通讯作者：
Rui Luo;Buddhika Nettasinghe;V. Krishnamurthy
中科院分区：
文献类型：
--
作者：
Rui Luo;Buddhika Nettasinghe;V. Krishnamurthy
本文讨论了社会网络中被称为回音室和隔离的社会学现象的建模和估计。具体地说，我们提出了一种新的基于社区的图模型，该模型将分离回音室的出现表示为马尔科夫桥(MB)过程。MB是一个一维马尔可夫随机场，它有助于在确定的时间对社区的形成和分离进行建模，这在具有已知时间事件的社交网络中是重要的。我们用真实世界的例子证明了所提出的模型的合理性，并在最近的Twitter数据集上检验了它的性能。我们提出了一种基于最大似然的模型参数估计算法和一种贝叶斯滤波算法，用于利用从网络获得的含噪声样本递归地估计分离程度。数值结果表明，该算法在均方误差方面优于传统的隐马尔可夫模型。所提出的滤波方法在计算社会科学中是有用的，在计算社会科学中，需要数据驱动地估计与噪声数据的分离程度。
This article 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 (MB) process. An MB is a 1-D 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 real-world examples and examine its performance on a recent Twitter dataset. We provide a 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.