Prominent Features of Rumor Propagation in Online Social Media

Kwon, Sejeong; Cha, Meeyoung; Jung, Kyomin; Chen, Wei; Wang, Yajun

doi:10.1109/icdm.2013.61

Cited by 631 publications

(356 citation statements)

References 9 publications

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“…Targeting these influential spreaders in information propagation is significant for the development of the approaches for either quickening the speed of propagation such as the application of viral marketing [8][9][10] or blocking the diffusion of undesirable information, such as rumors and viruses [11][12][13].Therefore, several algorithms have been proposed to identify the most influential spreaders in OSNs. The output of recent researches in identifying influential spreaders in OSNs has triggered an extensive debate.…”

Section: Introductionmentioning

confidence: 99%

“…Spreading of information influentially is a pervasive process; it refers to variety of applications [8][9][10][11][12][13]. Targeting these influential spreaders in information propagation is significant for the development of the approaches for either quickening the speed of propagation such as the application of viral marketing [8][9][10] or blocking the diffusion of undesirable information, such as rumors and viruses [11][12][13].Therefore, several algorithms have been proposed to identify the most influential spreaders in OSNs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi

Varathan

Ravana

et al. 2016

IFS

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Abstract. Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying the influential spreaders in multilayer interactions of online social networks

Al-Garadi

Varathan

Ravana

et al. 2016

IFS

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“…Examples include epidemics spreading and strategies to arrest their spread [1][2][3], the evolution of the electoral map during elections [4], the spreading of rumors [5], memes [6,7], and opinions [8], the migration patterns of banknotes [9] and human populations [10], and the effects of cities and infrastructure layouts on commerce and productivity [11,12]. Many of these questions require specific knowledge of individuals' geographical location as well as their social contacts (many infections propagate by direct contact, or physical proximity; we discuss and influence the opinions of mostly those close to us, etc.).…”

Section: Introductionmentioning

confidence: 99%

Modeling Spatial Social Complex Networks for Dynamical Processes

et al. 2018

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The study of social networks-where people are located, geographically, and how they might be connected to one another-is a current hot topic of interest, because of its immediate relevance to important applications, from devising efficient immunization techniques for the arrest of epidemics to the design of better transportation and city planning paradigms to the understanding of how rumors and opinions spread and take shape over time. We develop a Spatial Social Complex Network (SSCN) model that captures not only essential connectivity features of real-life social networks, including a heavy-tailed degree distribution and high clustering, but also the spatial location of individuals, reproducing Zipf 's law for the distribution of city populations as well as other observed hallmarks. We then simulate Milgram's Small-World experiment on our SSCN model, obtaining good qualitative agreement with the known results and shedding light on the role played by various network attributes and the strategies used by the players in the game. This demonstrates the potential of the SSCN model for the simulation and study of the many social processes mentioned above, where both connectivity and geography play a role in the dynamics.

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“…Several publications have focused on studying characteristics of rumor propagation, analyzing features [5,11,18,7] and proposing diffusion models [4,12,21,20,1]. Great effort has been devoted to the creation of effective classifiers to detect false content or fake accounts, highlighting recurrent patterns [10,6,14,18]. On the other hand, several theories have been proposed to limit the diffusion of hoaxes, identifying the most influential users or working on prejudices and personal beliefs, from a psychological point of view [3,9].…”

Section: Introductionmentioning

confidence: 99%

Fact-checking Effect on Viral Hoaxes

Tambuscio¹,

Ruffo²,

Flammini

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

128

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The Internet and online social networks have greatly facilitated and accelerated information diffusion processes, but at the same time they provide fertile ground for the spread of misinformation, rumors and hoaxes. The goal of this work is to introduce a simple modeling framework to study the diffusion of hoaxes and in particular how the availability of debunking information may contain their diffusion. As traditionally done in the mathematical modeling of information diffusion processes, we regard hoaxes as viruses: users can become infected if they are exposed to them, and turn into spreaders as a consequence. Upon verification, users can also turn into non-believers and spread the same attitude with a mechanism analogous to that of the hoax-spreaders. Both believers and non-believers, as time passes, can return to a susceptible state. Our model is characterized by four parameters: spreading rate, gullibility, probability to verify a hoax, and that to forget one's current belief. Simulations on homogeneous, heterogeneous, and real networks for a wide range of parameters values reveal a threshold for the factchecking probability that guarantees the complete removal of the hoax from the network. Via a mean field approximation, we establish that the threshold value does not depend on the spreading rate but only on the gullibility and forgetting probability. Our approach allows to quantitatively gauge the minimal reaction necessary to eradicate a hoax.

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Prominent Features of Rumor Propagation in Online Social Media

Cited by 631 publications

References 9 publications

Identifying the influential spreaders in multilayer interactions of online social networks

Identifying the influential spreaders in multilayer interactions of online social networks

Modeling Spatial Social Complex Networks for Dynamical Processes

Fact-checking Effect on Viral Hoaxes

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