基于结构矩阵分解的分层重叠置信度估计
Hierarchical Overlapping Belief Estimation by Structured Matrix Factorization
摘要：
许多关于社交媒体意见极化的工作都集中在对来自媒体痕迹的不同社区的立场（或正交信仰）进行扁平分类。我们在这项工作中扩展了两个重要方面。首先，我们不仅发现社区之间的分歧点，而且还发现协议点。换句话说，我们在存在重叠的情况下估计社区信仰。第二，代替平面分类，我们考虑分层的信念估计，社区可能是分层划分。例如，两个对立的政党可能在核心问题上存在分歧，但在一个政党内部，尽管在基本问题上达成了一致，但在进一步的细节上可能会出现分歧。我们称由此产生的组合问题的层次重叠的信念估计问题。为了解决这个问题，本文提出了一类新的无监督非负矩阵分解算法，我们称之为信念结构矩阵分解算法。我们提出的无监督算法捕获了潜在的信念交叉点和相异点，以及层次结构。我们讨论了该算法的性能，并在合成和真实世界的数据集上进行了评估。在合成数据集中，我们的模型将误差降低了40%。在真实的Twitter跟踪中，它将准确率提高了约10%。该模型在健全性检查中也达到了96.08%的自一致性。
英文摘要：
Much work on social media opinion polarization focuses on a flat categorization of stances (or orthogonal beliefs) of different communities from media traces. We extend in this work in two important respects. First, we detect not only points of disagreement between communities, but also points of agreement. In other words, we estimate community beliefs in the presence of overlap. Second, in lieu of flat categorization, we consider hierarchical belief estimation, where communities might be hierarchically divided. For example, two opposing parties might disagree on core issues, but within a party, despite agreement on fundamentals, disagreement might occur on further details. We call the resulting combined problem a hierarchical overlapping belief estimation problem. To solve it, this paper develops a new class of unsupervised Non-negative Matrix Factorization (NMF) algorithms, we call Belief Structured Matrix Factorization (BSMF). Our proposed unsupervised algorithm captures both the latent belief intersections and dissimilarities, as well as a hierarchical structure. We discuss the properties of the algorithm and evaluate it on both synthetic and real-world datasets. In the synthetic dataset, our model reduces error by 40%. In real Twitter traces, it improves accuracy by around 10%. The model also achieves 96.08% self-consistency in a sanity check.
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