Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

Teng, Xian; Pei, Sen; Morone, Flaviano; Makse, Hernán A.

doi:10.1038/srep36043

Cited by 55 publications

(42 citation statements)

References 45 publications

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“…The fragmentation of the network is measured by the size of the largest connected component, called the giant component of the network. The CI algorithm considers influence as an emergent collective property, not as a local property such as the node's degree, and has been shown to be able to identify super-spreaders of information in social networks [40,41]. Here, we consider a directed version of the algorithm where we target the super-sources of information.…”

Section: Collective Influence Algorithm In Directed Networkmentioning

confidence: 99%

Influence of fake news in Twitter during the 2016 US presidential election

2019

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The dynamics and influence of fake news on Twitter during the 2016 US presidential election remains to be clarified. Here, we use a dataset of 171 million tweets in the five months preceding the election day to identify 30 million tweets, from 2.2 million users, which contain a link to news outlets. Based on a classification of news outlets curated by www.opensources.co, we find that 25% of these tweets spread either fake or extremely biased news. We characterize the networks of information flow to find the most influential spreaders of fake and traditional news and use causal modeling to uncover how fake news influenced the presidential election. We find that, while top influencers spreading traditional center and left leaning news largely influence the activity of Clinton supporters, this causality is reversed for the fake news: the activity of Trump supporters influences the dynamics of the top fake news spreaders.

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Section: Collective Influence Algorithm In Directed Networkmentioning

confidence: 99%

Influence of fake news in Twitter during the 2016 US presidential election

2019

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“…Neglecting the multiplex structure of a network would lead to significant inaccuracies about its robustness. In applications, the collective influence theory has been used to locate superspreaders of information in real-world social media [113], find sources of fake news in Twitter during the 2016 US presidential election [114,115], single out critical regions in brain networks [10,116], infer personal economic status [117], improve cooperation in evolutionary games [118] and control biological networks [119,120,121,122].…”

Section: Collective Influencementioning

confidence: 99%

Influencer identification in dynamical complex systems

Pei

Wang

Morone

et al. 2019

Journal of Complex Networks

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The integrity and functionality of many real-world complex systems hinge on a small set of pivotal nodes, or influencers. In different contexts, these influencers are defined as either structurally important nodes that maintain the connectivity of networks, or dynamically crucial units that can disproportionately impact certain dynamical processes. In practice, identification of the optimal set of influencers in a given system has profound implications in a variety of disciplines. In this review, we survey recent advances in the study of influencer identification developed from different perspectives, and present state-of-the-art solutions designed for different objectives. In particular, we first discuss the problem of finding the minimal number of nodes whose removal would breakdown the network (i.e., the optimal percolation or network dismantle problem), and then survey methods to locate the essential nodes that are capable of shaping global dynamics with either continuous (e.g., independent cascading models) or discontinuous phase transitions (e.g., threshold models). We conclude the review with a summary and an outlook.

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“…Once we characterized activation of each user as being endogenously or exogenously driven, we can estimate the extent to which each user contributed to the activation of its peers by excluding the portion of the influence attributed to exogenous factors. We do not have a deterministic propagation path for our activation cascade -we do not know who influenced whom directly, so we cannot deterministically incorporate influence of all users in a transitive manner 48 . Nevertheless, our measure of influence simply incorporates all possible endogenous propagation paths to estimate an influence for each user (Figure 5a and Equation 5).…”

Section: Collective Influencementioning

confidence: 99%

Disentangling Sources of Influence in Online Social Networks

2019

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Information propagation in online social networks is facilitated by two types of influence -endogenous (peer) influence that acts between users of the social network and exogenous (external) that corresponds to various external mediators such as online news media. However, inference of these influences from data remains a challenge, especially when data on the activation of users is scarce. In this paper we propose a methodology that yields estimates of both endogenous and exogenous influence using only a social network structure and a single activation cascade. Our method exploits the statistical differences between the two types of influence -endogenous is dependent on the social network structure and current state of each user while exogenous is independent of these. We evaluate our methodology on simulated activation cascades as well as on cascades obtained from several large Facebook political survey applications. We show that our methodology is able to provide estimates of endogenous and exogenous influence in online social networks, characterize activation of each individual user as being endogenously or exogenously driven, and identify most influential groups of users.ABSTRACT This is the Supplementary information for the paper "Disentangling sources of influence in online social networks". It contains more detailed explanations of the methodology and descriptions and interpretation of additional experiments which were not included in the main paper due to space restrictions.

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Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

Cited by 55 publications

References 45 publications

Influence of fake news in Twitter during the 2016 US presidential election

Influence of fake news in Twitter during the 2016 US presidential election

Influencer identification in dynamical complex systems

Disentangling Sources of Influence in Online Social Networks

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