Disentangling Overlapping Beliefs by Structured Matrix Factorization
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by
Chaoqi Yang, Jinyang Li, Ruijie Wang, Shuochao Yao, Huajie Shao,
Dongxin Liu, Shengzhong Liu, Tianshi Wang, Tarek F. Abdelzaher
2020
Abstract
Much work on social media opinion polarization focuses on identifying
separate or orthogonal beliefs from media traces, thereby missing points of
agreement among different communities. This paper develops a new class of
Non-negative Matrix Factorization (NMF) algorithms that allow identification of
both agreement and disagreement points when beliefs of different communities
partially overlap. Specifically, we propose a novel Belief Structured Matrix
Factorization algorithm (BSMF) to identify partially overlapping beliefs in
polarized public social media. BSMF is totally unsupervised and considers three
types of information: (i) who posted which opinion, (ii) keyword-level message
similarity, and (iii) empirically observed social dependency graphs (e.g.,
retweet graphs), to improve belief separation. In the space of unsupervised
belief separation algorithms, the emphasis was mostly given to the problem of
identifying disjoint (e.g., conflicting) beliefs. The case when individuals
with different beliefs agree on some subset of points was less explored. We
observe that social beliefs overlap even in polarized scenarios. Our proposed
unsupervised algorithm captures both the latent belief intersections and
dissimilarities. We discuss the properties of the algorithm and conduct
extensive experiments on both synthetic data and real-world datasets. The
results show that our model outperforms all compared baselines by a great
margin.
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