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A Distance Measure for the Analysis of Polar Opinion Dynamics in Social Networks

Authors:

Victor Amelkin,

Petko Bogdanov,

Ambuj K. SinghAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 13, Issue 4

Article No.: 38, Pages 1 - 34

https://doi.org/10.1145/3332168

Published: 08 August 2019 Publication History

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Abstract

Analysis of opinion dynamics in social networks plays an important role in today’s life. For predicting users’ political preference, it is particularly important to be able to analyze the dynamics of competing polar opinions, such as pro-Democrat vs. pro-Republican. While observing the evolution of polar opinions in a social network over time, can we tell when the network evolved abnormally? Furthermore, can we predict how the opinions of the users will change in the future? To answer such questions, it is insufficient to study individual user behavior, since opinions can spread beyond users’ ego-networks. Instead, we need to consider the opinion dynamics of all users simultaneously and capture the connection between the individuals’ behavior and the global evolution pattern of the social network.

In this work, we introduce the Social Network Distance (SND)—a distance measure that quantifies the likelihood of evolution of one snapshot of a social network into another snapshot under a chosen model of polar opinion dynamics. SND has a rich semantics of a transportation problem, yet, is computable in time linear in the number of users and, as such, is applicable to large-scale online social networks. In our experiments with synthetic and Twitter data, we demonstrate the utility of our distance measure for anomalous event detection. It achieves a true positive rate of 0.83, twice as high as that of alternatives. The same predictions presented in precision-recall space show that SND retains perfect precision for recall up to 0.2. Its precision then decreases while maintaining more than 2-fold improvement over alternatives for recall up to 0.95. When used for opinion prediction in Twitter data, SND’s accuracy is 75.6%, which is 7.5% higher than that of the next best method.

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Cited By

Lan SYu YLong J(2023)Application of Big Data-based Text Processing Technology in Public Opinion Monitoring System in Universities2023 International Conference on Intelligent Communication and Networking (ICN)10.1109/ICN60549.2023.10426298(191-195)Online publication date: 10-Nov-2023
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https://doi.org/10.1098/rstb.2021.0382
Abbas A(2021)Social network analysis using deep learning: applications and schemesSocial Network Analysis and Mining10.1007/s13278-021-00799-z11:1Online publication date: 25-Oct-2021
https://doi.org/10.1007/s13278-021-00799-z

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 13, Issue 4

August 2019

235 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3343141

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 08 August 2019

Accepted: 01 May 2019

Revised: 01 May 2019

Received: 01 May 2018

Published in TKDD Volume 13, Issue 4

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U. S. Army Research Laboratory
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Cited By

Lan SYu YLong J(2023)Application of Big Data-based Text Processing Technology in Public Opinion Monitoring System in Universities2023 International Conference on Intelligent Communication and Networking (ICN)10.1109/ICN60549.2023.10426298(191-195)Online publication date: 10-Nov-2023
https://doi.org/10.1109/ICN60549.2023.10426298
Farahbakhsh IBauch CAnand M(2022)Modelling coupled human–environment complexity for the future of the biosphere: strengths, gaps and promising directionsPhilosophical Transactions of the Royal Society B: Biological Sciences10.1098/rstb.2021.0382377:1857Online publication date: 27-Jun-2022
https://doi.org/10.1098/rstb.2021.0382
Abbas A(2021)Social network analysis using deep learning: applications and schemesSocial Network Analysis and Mining10.1007/s13278-021-00799-z11:1Online publication date: 25-Oct-2021
https://doi.org/10.1007/s13278-021-00799-z

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