Data Mining and Knowledge Discovery

, Volume 31, Issue 5, pp 1480–1505 | Cite as

Measuring and moderating opinion polarization in social networks

  • Antonis MatakosEmail author
  • Evimaria Terzi
  • Panayiotis Tsaparas
Part of the following topical collections:
  1. Journal Track of ECML PKDD 2017


The polarization of society over controversial social issues has been the subject of study in social sciences for decades (Isenberg in J Personal Soc Psychol 50(6):1141–1151, 1986, Sunstein in J Polit Philos 10(2):175–195, 2002). The widespread usage of online social networks and social media, and the tendency of people to connect and interact with like-minded individuals has only intensified the phenomenon of polarization (Bakshy et al. in Science 348(6239):1130–1132, 2015). In this paper, we consider the problem of measuring and reducing polarization of opinions in a social network. Using a standard opinion formation model (Friedkin and Johnsen in J Math Soc 15(3–4):193–206, 1990), we define the polarization index, which, given a network and the opinions of the individuals in the network, it quantifies the polarization observed in the network. Our measure captures the tendency of opinions to concentrate in network communities, creating echo-chambers. Given this numeric measure of polarization, we then consider the problem of reducing polarization in the network by convincing individuals (e.g., through education, exposure to diverse viewpoints, or incentives) to adopt a more neutral stand towards controversial issues. We formally define the ModerateInternal and ModerateExpressed problems, and we prove that both our problems are NP-hard. By exploiting the linear-algebraic characteristics of the opinion formation model we design polynomial-time algorithms for both problems. Our experiments with real-world datasets demonstrate the validity of our metric, and the efficiency and the effectiveness of our algorithms in practice.


Polarization Social networks Opinion formation Moderation 



This work was supported by the Marie Curie Reintegration Grant projects titled JMUGCS which has received research funding from the European Union, and the National Science Foundation grants: IIS 1320542, IIS 1421759, CAREER 1253393, as well as a gift from Microsoft. We would also like to thank Evaggelia Pitoura for useful comments and discussions on early drafts of the paper.


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Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringUniversity of IoanninaIoanninaGreece
  2. 2.Department of Computer ScienceBoston UniversityBostonUSA

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