Computational Modeling of Hierarchically Polarized Groups by Structured Matrix Factorization

The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of agreement and disagreement between groups, and 2) divides them hierarchically to represent nested patterns of agreement and disagreement given a structural...

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Published in:Frontiers in big data Vol. 4; p. 729881
Main Authors: Sun, Dachun, Yang, Chaoqi, Li, Jinyang, Wang, Ruijie, Yao, Shuochao, Shao, Huajie, Liu, Dongxin, Liu, Shengzhong, Wang, Tianshi, Abdelzaher, Tarek F.
Format: Journal Article
Language:English
Published: Switzerland Frontiers Media S.A 22.12.2021
Subjects:
belief estimation
Big Data
hierarchical
matrix factorization
polarization
unsupervised
matrix factorization
polarization
belief estimation
unsupervised
hierarchical
ISSN:2624-909X, 2624-909X
Online Access:Get full text
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Summary:The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of agreement and disagreement between groups, and 2) divides them hierarchically to represent nested patterns of agreement and disagreement given a structural guide. For example, two opposing parties might disagree on core issues. Moreover, within a party, despite agreement on fundamentals, disagreement might occur on further details. We call such scenarios hierarchically polarized groups . An (enhanced) unsupervised Non-negative Matrix Factorization (NMF) algorithm is described for computational modeling of hierarchically polarized groups. It is enhanced with a language model, and with a proof of orthogonality of factorized components. We evaluate it on both synthetic and real-world datasets, demonstrating ability to hierarchically decompose overlapping beliefs. In the case where polarization is flat, we compare it to prior art and show that it outperforms state of the art approaches for polarization detection and stance separation. An ablation study further illustrates the value of individual components, including new enhancements.
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Edited by: Neil Shah, Independent researcher, Santa Monica, CA, United States
Reviewed by: Tong Zhao, University of Notre Dame, United States
These authors have contributed equally to this work
This article was submitted to Big Data Networks, a section of the journal Frontiers in Big Data
Ekta Gujral, Walmart Labs, United States
ISSN:2624-909X
2624-909X
DOI:10.3389/fdata.2021.729881