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The polarizing effect of network influences

Published: 01 June 2014 Publication History

Abstract

In social networks, opinions and behaviors tend to spread quickly. When an idea seeks to gain attention, success requires both attracting individual users and a careful understanding of cascading behavior -- an idea that attracts a small set of highly influential individuals can easily overwhelm an idea with a much larger, but less influential, support base. Understanding exactly how the choices of individuals propagate through a network, however, poses significant challenges. In this work, we consider a model recently studied by Chierichetti, Kleinberg, and Panconesi (EC 2012) to model cascading behavior when members of a social network must each choose one of two opposing ideas. The model captures the struggle between a desire to follow personal preferences and to match the choices of those you interact with.
In this model, observed choices can look much different than the underlying preferences of individuals in the social network, due to cascading of behavior from individuals following their neighbors' lead. In this work, we seek to understand how these quantities can differ. We give strong bounds on adoption rates in terms of underlying preferences, strengthening results of the aforementioned work. Furthermore, our results hold both for richer types of influence between individuals and under weaker assumptions on the underlying preferences of individuals than those previously studied. Notably, we derive bounds that are robust to certain types of correlation between the personal preferences of agents, allowing for our results to be applied to a wider range of settings than prior works which required complete independence between individuals.

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  • (2018)Game-Theoretic Cross Social Media Analytic: How Yelp Ratings Affect Deal Selection on Groupon?IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.277949430:5(908-921)Online publication date: 1-May-2018
  • (2017)Hidden Chinese Restaurant Game: Grand Information Extraction for Stochastic Network LearningIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2017.26827993:2(330-345)Online publication date: Jun-2017

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    cover image ACM Conferences
    EC '14: Proceedings of the fifteenth ACM conference on Economics and computation
    June 2014
    1028 pages
    ISBN:9781450325653
    DOI:10.1145/2600057
    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 ACM 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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    Published: 01 June 2014

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    Author Tags

    1. behavioral cascades
    2. sequential decisions
    3. social networks

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    EC '14: ACM Conference on Economics and Computation
    June 8 - 12, 2014
    California, Palo Alto, USA

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    Overall Acceptance Rate 664 of 2,389 submissions, 28%

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    View all
    • (2018)Game-Theoretic Cross Social Media Analytic: How Yelp Ratings Affect Deal Selection on Groupon?IEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.277949430:5(908-921)Online publication date: 1-May-2018
    • (2017)Hidden Chinese Restaurant Game: Grand Information Extraction for Stochastic Network LearningIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2017.26827993:2(330-345)Online publication date: Jun-2017

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