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

Bovet, Alexandre; Makse, Hernán A.

doi:10.1038/s41467-018-07761-2

Cited by 711 publications

(543 citation statements)

References 43 publications

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“…where k i is the degree of node i, d i j is length of the shortest distance between nodes i and j, and l is the metric's parameter. In line with Bovet and Makse (2019), we consider only node indegree in the computation of CI as node indegree is indicative of node impact. Using node outdegree or combining in-and out-degree leads to inferior results in our evaluation.…”

Section: Collective Influence (Ci)mentioning

confidence: 99%

Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data

Lü

Medo

2020

Journal of Informetrics

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Despite the increasing use of citation-based metrics for research evaluation purposes, we do not know yet which metrics best deliver on their promise to gauge the significance of a scientific paper or a patent. We assess 17 networkbased metrics by their ability to identify milestone papers and patents in three large citation datasets. We find that traditional information-retrieval evaluation metrics are strongly affected by the interplay between the age distribution of the milestone items and age biases of the evaluated metrics. Outcomes of these metrics are therefore not representative of the metrics' ranking ability. We argue in favor of a modified evaluation procedure that explicitly penalizes biased metrics and allows us to reveal metrics' performance patterns that are consistent across the datasets. PageRank and LeaderRank turn out to be the best-performing ranking metrics when their age bias is suppressed by a simple transformation of the scores that they produce, whereas other popular metrics, including citation count, HITS and Collective Influence, produce significantly worse ranking results.

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Section: Collective Influence (Ci)mentioning

confidence: 99%

Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data

Lü

Medo

2020

Journal of Informetrics

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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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“…978-1-4503-7024-0/20/04. DOI: 10.1145/3366424.3385775 Although propaganda, misinformation, and in uence campaigns have always existed [8], we have been witnessing a considerable increase in state-sponsored cyber warfare [14] -sometimes internally via censorship and propaganda (e.g., China [1]), sometimes across borders by promoting polarization and disinformation (e.g., Russia in the 2016 US elections [4,7]).…”

Section: Introductionmentioning

confidence: 99%

Unveiling Coordinated Groups Behind White Helmets Disinformation

Pacheco

Flammini

Menczer

2020

Companion Proceedings of the Web Conference 2020

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Propaganda, disinformation, manipulation, and polarization are the modern illnesses of a society increasingly dependent on social media as a source of news. In this paper, we explore the disinformation campaign, sponsored by Russia and allies, against the Syria Civil Defense (a.k.a. the White Helmets). We unveil coordinated groups using automatic retweets and content duplication to promote narratives and/or accounts. e results also reveal distinct promoting strategies, ranging from the small groups sharing the exact same text repeatedly, to complex "news website factories" where dozens of accounts synchronously spread the same news from multiple sites.

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

Cited by 711 publications

References 43 publications

Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data

Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data

Influencer identification in dynamical complex systems

Unveiling Coordinated Groups Behind White Helmets Disinformation

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