Modeling opinion dynamics in social networks

Conference Paper

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. © 2014 ACM.

Full Text

Duke Authors

Cited Authors

  • Das, A; Gollapudi, S; Munagala, K

Published Date

  • January 1, 2014

Published In

  • Wsdm 2014 Proceedings of the 7th Acm International Conference on Web Search and Data Mining

Start / End Page

  • 403 - 412

International Standard Book Number 13 (ISBN-13)

  • 9781450323512

Digital Object Identifier (DOI)

  • 10.1145/2556195.2559896

Citation Source

  • Scopus