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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| Vydané v: | Frontiers in big data Ročník 4; s. 729881 |
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| Jazyk: | English |
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Switzerland
Frontiers Media S.A
22.12.2021
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| ISSN: | 2624-909X, 2624-909X |
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| Abstract | 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. |
|---|---|
| AbstractList | 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.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. 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. The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of and between groups, and 2) divides them 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 . 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. 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. |
| Author | Li, Jinyang Yao, Shuochao Sun, Dachun Liu, Shengzhong Abdelzaher, Tarek F. Yang, Chaoqi Wang, Tianshi Liu, Dongxin Shao, Huajie Wang, Ruijie |
| AuthorAffiliation | 1 Computer Science Department, University of Illinois at Urbana-Champaign , Urbana , IL , United States 3 Computer Science Department, William and Mary , Williamsburg , VA , United States 2 Computer Science Department, George Mason University , Fairfax , VA , United States |
| AuthorAffiliation_xml | – name: 3 Computer Science Department, William and Mary , Williamsburg , VA , United States – name: 1 Computer Science Department, University of Illinois at Urbana-Champaign , Urbana , IL , United States – name: 2 Computer Science Department, George Mason University , Fairfax , VA , United States |
| Author_xml | – sequence: 1 givenname: Dachun surname: Sun fullname: Sun, Dachun – sequence: 2 givenname: Chaoqi surname: Yang fullname: Yang, Chaoqi – sequence: 3 givenname: Jinyang surname: Li fullname: Li, Jinyang – sequence: 4 givenname: Ruijie surname: Wang fullname: Wang, Ruijie – sequence: 5 givenname: Shuochao surname: Yao fullname: Yao, Shuochao – sequence: 6 givenname: Huajie surname: Shao fullname: Shao, Huajie – sequence: 7 givenname: Dongxin surname: Liu fullname: Liu, Dongxin – sequence: 8 givenname: Shengzhong surname: Liu fullname: Liu, Shengzhong – sequence: 9 givenname: Tianshi surname: Wang fullname: Wang, Tianshi – sequence: 10 givenname: Tarek F. surname: Abdelzaher fullname: Abdelzaher, Tarek F. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35005618$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1145/2488388.2488442 10.1016/j.knosys.2018.01.019 10.1371/journal.pone.0159641 10.1145/2433396.2433471 10.1145/3369026 10.1109/INFOCOM.2017.8056959 10.1007/s10115-017-1081-x 10.1609/aaai.v31i1.11008 10.1109/ICDCS.2013.37 10.1016/j.physrep.2016.09.002 10.1145/2396761.2396843 10.1109/ISSPIT.2009.5407557 10.3115/v1/D14-1080 10.1016/j.patcog.2008.08.026 10.1609/icwsm.v8i1.14524 10.1109/MILCOM.2012.6415602 10.1609/aaai.v31i1.10974 10.7551/mitpress/7503.003.0205 10.1002/9780470747278 10.1002/env.3170050203 10.1109/ICDCS.2017.289 10.1007/s10618-006-0059-1 10.1145/3219819.3220064 10.1109/tip.2011.2105496 10.1109/ICDM.2008.57 10.1109/TC.2020.3008561 10.2200/S00416ED1V01Y201204HLT016 10.1109/ICASSP.2005.1416297 10.1016/j.patrec.2011.01.012 10.1126/science.aaa1160 10.1145/2700171.2791038 10.18653/v1/D15-1168 |
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| Copyright | Copyright © 2021 Sun, Yang, Li, Wang, Yao, Shao, Liu, Liu, Wang and Abdelzaher. Copyright © 2021 Sun, Yang, Li, Wang, Yao, Shao, Liu, Liu, Wang and Abdelzaher. 2021 Sun, Yang, Li, Wang, Yao, Shao, Liu, Liu, Wang and Abdelzaher |
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| Keywords | matrix factorization polarization belief estimation unsupervised hierarchical |
| Language | English |
| License | Copyright © 2021 Sun, Yang, Li, Wang, Yao, Shao, Liu, Liu, Wang and Abdelzaher. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 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 |
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| Snippet | The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of
agreement
and... The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of and between... The paper extends earlier work on modeling hierarchically polarized groups on social media. An algorithm is described that 1) detects points of agreement and... |
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| SubjectTerms | belief estimation Big Data hierarchical matrix factorization polarization unsupervised |
| Title | Computational Modeling of Hierarchically Polarized Groups by Structured Matrix Factorization |
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