A structured matrix factorization method for computational modeling of hierarchical polarization in social interactions

Many works on social interaction polarization detection focus heavily on flat classification of stances and beliefs. We extend them in this work in two important aspects: (i) detects both points of agreement and disagreement between groups, and (ii) 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, a disagreement might occur on further details within a party, despite agreement on the fundamentals. We call such scenarios hierarchically polarization. An unsupervised Non-negative Matrix Factorization (NMF) algorithm is described for the computational modeling of hierarchical polarization in social interactions. The algorithm is enhanced with a language model and a proof of orthogonality of factorized components. We evaluate it on both synthetic and real-world datasets, demonstrating the ability to decompose overlapping beliefs hierarchically. In the case where polarization is flat, we compare it to the 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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Sun, Dachun

Contributors dc:contributor

• Abdelzaher, Tarek

• Polarization
• Belief Estimation
• Hierarchical
• Matrix Factorization
• Unsupervised

Statement dc:rights

• Copyright 2023 Dachun Sun

Language dc:language

en, eng

Handle dc:identifier

https://hdl.handle.net/2142/120391

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F120391