Rumor Spreading in Social Networks

Chierichetti, Flavio; Lattanzi, Silvio; Panconesi, Alessandro

doi:10.1007/978-3-642-02930-1_31

Cited by 85 publications

(100 citation statements)

References 21 publications

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“…In online social networks, reducing connectivity can slow down or even stop rumor spreading. Chierichetti et al studied the performance of rumor spreading in the classic preferential attachment model and compared the efficiency of disseminating information among different strategies: the standard PUSH-PULL, PUSH, and PULL strategies [6]. In email networks, viruses can be transmitted quickly through attachments.…”

Section: Related Workmentioning

confidence: 98%

Efficient Link Cuts in Online Social Networks

Ruan

Deng

Amariucai

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

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Due to the huge popularity of online social networks, many researchers focus on adding links, e.g., link prediction to help friend recommendation. So far, no research has been performed on link cuts. However, the spread of malware and misinformation can cause havoc and hence it is interesting to see how to cut links such that malware and misinformation will not run rampant. In fact, many online social networks can be modeled as undirected graphs. In this paper, we investigate different strategies to cut links among different users in undirected graphs so that the speed of virus and misinformation spread can be slowed down the most or even cut off. Two measures are chosen to evaluate the performance of these strategies: Average Inverse of Shortest Path Length (AIPL) and Rumor Saturation Rate (RSR). AIPL measures the communication efficiency of the whole graph while RSR checks the percentage of users receiving information within a certain time interval.

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Section: Related Workmentioning

confidence: 98%

Efficient Link Cuts in Online Social Networks

Ruan

Deng

Amariucai

et al. 2015

2015 IEEE Global Communications Conference (GLOBECOM)

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“…In addition, complex networks have been used to model individuals (nodes) and their interactions (vertices) in a population propagating a rumor. 7,8 Moreover, some effort has been made toward examining the similarity between disease and rumor propagation. 9,10 The consumer market proposed has some characteristics of epidemic spreading models and information/rumor spreading in a population.…”

Section: Introductionmentioning

confidence: 99%

On estimating the impact of information spreading in a consumer market modeled by probabilistic cellular automata and ordinary differential equations

2017

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Individuals spread information about their lives, their career, and their recently purchased items. In addition, considering that the customer's opinions may influence their friendship circle, neighbors, and work colleagues, they constitute a very important factor to consider in future actions of companies. In this paper, we present an approach based on probabilistic cellular automata and ordinary differential equations for modeling the consumer market, inspired by a biological system of an infectious disease-spreading model. However, instead of spreading a disease, consumers spread their opinion to other individuals, thereby having consequences on sales levels. Using marketing, the economic situation, and individual opinion as input variables of the consumer market model, simulation output (generated demand) is then used in the business game, helping the mediator (teacher) to create different scenarios and giving feedback to the student at simulation runtime, even if many players are involved in the game. Analytical and numerical simulations show that the proposed approach allows the teacher to create virtual markets for the players just by setting the parameters that describe a scenario, increasing the dynamism of the game, saving time, and allowing the mediator to focus on other important subjects, such as the teaching concepts of business strategies and decision support. Moreover, simulations are conducted to demonstrate the applicability of the proposed model in real life problems, such as the electro-domestic market.

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“…In the last few years there has been a growing interest in this topic, with different approaches and techniques. 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].…”

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

134

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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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Rumor Spreading in Social Networks

Cited by 85 publications

References 21 publications

Efficient Link Cuts in Online Social Networks

Efficient Link Cuts in Online Social Networks

On estimating the impact of information spreading in a consumer market modeled by probabilistic cellular automata and ordinary differential equations

Fact-checking Effect on Viral Hoaxes

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