Minimizing polarization and disagreement in social networks

Cameron Musco, Christopher Musco, Charalampos E. Tsourakakis

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The rise of social media and online social networks has been a disruptive force in society. Opinions are increasingly shaped by interactions on online social media, and social phenomena including disagreement and polarization are now tightly woven into everyday life. In this work we initiate the study of the following question: \beginquotation \noindent Given n agents, each with its own initial opinion that reflects its core value on a topic, and an opinion dynamics model, what is the structure of a social network that minimizes \em disagreementand \em controversy simultaneously? \endquotation \noindent This question is central to recommender systems: should a recommender system prefer a link suggestion between two online users with similar mindsets in order to keep disagreement low, or between two users with different opinions in order to expose each to the others viewpoint of the world, and decrease overall levels of polarization and controversy? Such decisions have an important global effect on society \citewilliams2007social. Our contributions include a mathematical formalization of this question as an optimization problem and an exact, time-efficient algorithm. We also prove that there always exists a graph with $O(n/ϵ^2)$ edges that is a $(1+ϵ)$ approximation to the optimum. Our formulation is an instance of optimization over \em graph topologies, see also \citeboyd2004fastest,daitch2009fitting,sun2006fastest. Furthermore, for a given graph, we show how to optimize the same objective over the agents» innate opinions in polynomial time. Finally, we perform an empirical study of our proposed methods on synthetic and real-world data that verify their value as mining tools to better understand the trade-off between of disagreement and polarization. We find that there is a lot of space to reduce both controversy and disagreement in real-world networks; for instance, on a Reddit network where users exchange comments on politics, our methods achieve a reduction in controversy and disagreement of the order $6.2 \times 10^4$.

    Original languageEnglish (US)
    Title of host publicationThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018
    PublisherAssociation for Computing Machinery, Inc
    Pages369-378
    Number of pages10
    ISBN (Electronic)9781450356398
    DOIs
    StatePublished - Apr 10 2018
    Event27th International World Wide Web, WWW 2018 - Lyon, France
    Duration: Apr 23 2018Apr 27 2018

    Publication series

    NameThe Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018

    Conference

    Conference27th International World Wide Web, WWW 2018
    CountryFrance
    CityLyon
    Period4/23/184/27/18

    Keywords

    • Opinion dynamics
    • Optimization
    • Polarization
    • Social networks

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Software

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