research-article

Modeling opinion dynamics in social networks

Authors:

Sreenivas Gollapudi,

Kamesh MunagalaAuthors Info & Claims

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

Pages 403 - 412

https://doi.org/10.1145/2556195.2559896

Published: 24 February 2014 Publication History

Abstract

Our opinions and judgments are increasingly shaped by what we read on social media -- whether they be tweets and posts in social networks, blog posts, or review boards. These opinions could be about topics such as consumer products, politics, life style, or celebrities. Understanding how users in a network update opinions based on their neighbor's opinions, as well as what global opinion structure is implied when users iteratively update opinions, is important in the context of viral marketing and information dissemination, as well as targeting messages to users in the network.

In this paper, we consider the problem of modeling how users update opinions based on their neighbors' opinions. We perform a set of online user studies based on the celebrated conformity experiments of Asch [1]. Our experiments are carefully crafted to derive quantitative insights into developing a model for opinion updates (as opposed to deriving psychological insights). We show that existing and widely studied theoretical models do not explain the entire gamut of experimental observations we make. This leads us to posit a new, nuanced model that we term the BVM. We present preliminary theoretical and simulation results on the convergence and structure of opinions in the entire network when users iteratively update their respective opinions according to the BVM. We show that consensus and polarization of opinions arise naturally in this model under easy to interpret initial conditions on the network.

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Cited By

Shirzadi MZehmakan ANejdl WAuer SCha MMoens MNajork M(2025)Do Stubborn Users Always Cause More Polarization and Disagreement? A Mathematical StudyProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703510(309-317)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703510
Wang HLi YChen J(2024)Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social NetworksEntropy10.3390/e2610085126:10(851)Online publication date: 8-Oct-2024
https://doi.org/10.3390/e26100851
Jiang DDai QLi HYang J(2024)Opinion dynamics based on social learning theoryThe European Physical Journal B10.1140/epjb/s10051-024-00838-697:12Online publication date: 9-Dec-2024
https://doi.org/10.1140/epjb/s10051-024-00838-6
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Index Terms

Modeling opinion dynamics in social networks
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology

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cover image ACM Conferences

WSDM '14: Proceedings of the 7th ACM international conference on Web search and data mining

February 2014

712 pages

ISBN:9781450323512

DOI:10.1145/2556195

General Chairs:
Ben Carterette
University of Delaware, USA
,
Fernando Diaz
Microsoft Research, USA
,
Program Chairs:
Carlos Castillo
Qatar Computing Research Institute, Qatar
,
Donald Metzler
Google, USA

Copyright © 2014 ACM.

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Publication History

Published: 24 February 2014

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WSDM 2014: Seventh ACM International Conference on Web Search and Data Mining

February 24 - 28, 2014

New York, New York, USA

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WSDM '14 Paper Acceptance Rate 64 of 355 submissions, 18%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Shirzadi MZehmakan ANejdl WAuer SCha MMoens MNajork M(2025)Do Stubborn Users Always Cause More Polarization and Disagreement? A Mathematical StudyProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703510(309-317)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703510
Wang HLi YChen J(2024)Three-Stage Cascade Information Attenuation for Opinion Dynamics in Social NetworksEntropy10.3390/e2610085126:10(851)Online publication date: 8-Oct-2024
https://doi.org/10.3390/e26100851
Jiang DDai QLi HYang J(2024)Opinion dynamics based on social learning theoryThe European Physical Journal B10.1140/epjb/s10051-024-00838-697:12Online publication date: 9-Dec-2024
https://doi.org/10.1140/epjb/s10051-024-00838-6
Zhou XSun HXu WLi WZhang Z(2024)Friedkin-Johnsen Model for Opinion Dynamics on Signed GraphsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342497436:12(8313-8327)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3424974
Conjeaud ILorenz-Spreen PKalogeratos A(2024)DeGroot-Based Opinion Formation Under a Global Steering MechanismIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.333029311:3(4040-4057)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3330293
Xu WZhang Z(2024)Linear Opinion Dynamics Model With Higher Order InteractionsIEEE Transactions on Computational Social Systems10.1109/TCSS.2023.332414411:3(3627-3636)Online publication date: Jun-2024
https://doi.org/10.1109/TCSS.2023.3324144
Chen JLi YKou GWang H(2024)Effect of three-stage cascade of opinion dynamics models in coupled networksNeurocomputing10.1016/j.neucom.2023.127176572(127176)Online publication date: Mar-2024
https://doi.org/10.1016/j.neucom.2023.127176
Zareer MSelmic R(2024)Modeling interactions in social media networks using an asynchronous and synchronous opinion dynamicsSocial Network Analysis and Mining10.1007/s13278-024-01402-x14:1Online publication date: 13-Dec-2024
https://doi.org/10.1007/s13278-024-01402-x
Zhang ZXu WZhang ZChen G(2024)Opinion dynamics in social networks incorporating higher-order interactionsData Mining and Knowledge Discovery10.1007/s10618-024-01064-538:6(4001-4023)Online publication date: 30-Aug-2024
https://doi.org/10.1007/s10618-024-01064-5
Joseph GMurthy CJoseph GMurthy C(2024)Sparsity in Linear Dynamical SystemsSparsity-Constrained Linear Dynamical Systems10.1007/978-981-97-7090-8_1(1-13)Online publication date: 11-Dec-2024
https://doi.org/10.1007/978-981-97-7090-8_1
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