Spread of (mis)information in social networks

By Daron Acemoglu, Asuman E. Ozdaglar, Ali ParandehGheibi. Games Econ. Behav., 2009

532 citations recorded by the source corpus.

We provide a model to investigate the tension between information aggregation and spread of misinformation in large societies (conceptualized as networks of agents communicating with each other). Each individual holds a belief represented by a scalar. Individuals meet pairwise and exchange information, which is modeled as both individuals adopting the average of their pre-meeting beliefs. When all individuals engage in this type of information exchange, the society will be able to effectively aggregate the initial information held by all individuals. There is also the possibility of misinformation, however, because some of the individuals are "forceful," meaning that they influence the beliefs of (some) of the other individuals they meet, but do not change their own opinion. The paper characterizes how the presence of forceful agents interferes with information aggregation. Under the assumption that even forceful agents obtain some information (however infrequent) from some others (and additional weak regularity conditions), we first show that beliefs in this class of societies converge to a consensus among all individuals. This consensus value is a random variable, however, and we characterize its behavior. Our main results quantify the extent of misinformation in the society by either providing bounds or exact results (in some special cases) on how far the consensus value can be from the benchmark without forceful agents (where there is efficient information aggregation). The worst outcomes obtain when there are several forceful agents and forceful agents themselves update their beliefs only on the basis of information they obtain from individuals most likely to have received their own information previously.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/83e574e632fa85ae29b2603236473c6994b9973b

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In Management Science and Operations Research

In Statistical and Nonlinear Physics

Citations in the corpus

Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes (2016, background): “Additionally, researchers have proposed theoretical models of how rumors and misinformation propagate in social networks and how their spread may be contained [32, 1].”
Fighting an Infodemic: COVID-19 Fake News Dataset (2020, background): “In a similar way, several approaches have been developed to identify as-well-as limit the spread of (mis-)information (Acemoglu et al., 2010; Budak et al., 2011; Kwon et al., 2013; Ma et al., 2017; Nguyen et al., 2020; Chandra et al., 2020). Vo and Lee (2020) approach the problem of fake news spread”
Consensus Over Ergodic Stationary Graph Processes (2010, background): “Applications include distributed and parallel computing [1], motion coordination of autonomous agents [2], [3], distributed sensor fusion [4], as well as opinion dynamics [5] and belief formation in social networks [6], [ 7 ].”
Convergence Speed in Distributed Consensus and Averaging (2009, unknown): “The paper [22] explored applications of these types of models to a variety of situations in politics, and the related work [1] quantified the extent to which “stubborn” agents (who maintain fixed opinions) could influence the opinions of other agents.”
How Homophily Affects the Speed of Learning and Best Response Dynamics (2012, background): “in social networks, such as those studied by Acemoglu et al. (2011) and by”
False Information on Web and Social Media: A Survey (2018, background): “[3] presented one of the first models to simulate propagation of information in social media.”
Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate (2014, background): “As reported in [11], it is significantly more difficult to analyze social networks with several forceful agents that do not change their opinions and requires a different mathematical approach.”
Opinion Dynamics and Learning in Social Networks (2011, methodology): “We next consider a variation on the DeGroot model, developed in [1], which enables us to study the effect of prominent agents on the beliefs of the society.”
Binary Opinion Dynamics with Stubborn Agents (2013, background): “The diffusion of diseases are more commonly studied by Bailey [1975] and Kermack and McKendrick [1991] under susceptible, infected, susceptible (SIS) and susceptible, infected, removed (SIR) models, respectively.”
Non-Bayesian social learning (2012, unknown): “The crucial difference in results between Golub and Jackson (2010) and Acemoglu et al. (2010), on the one hand, and our model, on the other, is the role played by the social network in successful information aggregation.”
A Stylometric Inquiry into Hyperpartisan and Fake News (2017, background): “/ LOD Text catego rization D ecp tiond Context-based Social net work analysis Etzioni et al., 2008 M ag dy nW s, 2 01 Ginsca et al., 2015 Wu et al., 2014 C i amp g let ., 2015 Shi and Weninge r, 2016 Acemoglu et al., 2010 Bud ak et l., 20 1 Nguyen et al., 2012 Kwon et al., 2013 Mocanu et al., 2015 T”
Opinion Fluctuations and Disagreement in Social Networks (2010, background): “Existing models of communication and learning, based on Bayesian or non-Bayesian updating mechanisms, typically lead to consensus provided that communication takes place over a strongly connected network (e.g., Smith and Sorensen [43], Banerjee and Fudenberg [7], Acemoglu, Dahleh, Lobel and Ozdaglar”