Experimental Analysis of Rumor Spreading in Social Networks

Doerr, Benjamin; Fouz, Mahmoud; Friedrich, Tobias

doi:10.1007/978-3-642-34862-4_12

Cited by 19 publications

(21 citation statements)

References 30 publications

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“…It was shown in [5] that rumour diffuses slowly in the BA network as it takes polynomial time (Ω(N α ), for α > 0) for a single seed to spread the news to the whole network. In contrast, rumours spread quickly (in O(log N ) time) in other network models such as the random network model [7]. Therefore, intuitively, we expect the BA network to require a large number of nodes to achieve both high coverage and high coverage probability.…”

Section: Discussionmentioning

confidence: 92%

Minimizing Seed Selection for Disseminating News with Probabilistic Coverage Guarantee

Lee

Hsu

2015

Proceedings of the 17th International Conference on Electronic Commerce 2015

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The spread of influence under Independent Cascade model and Linear Threshold model has been studied extensively in the literature. The two models describe the process through which individuals adopt a new product. We study the rumour model, that describes the process through which a news is spread in a network. We consider the task of identifying a collection of seed nodes of minimal size to spread a news such that, within a certain time threshold, the news can be spread to a target number of nodes with certain probability. We show that the problem is NP-hard. Moreover, we show that the function that calculates the probability of achieving the coverage threshold is not submodular, and hence conventional optimization techniques based on the submodularity property cannot be applied directly. Nevertheless, we present an algorithm that can provably approximate the size of the optimal seed set. In addition, we present a Monte-Carlo algorithm for choosing the best node that generates the greatest marginal increment in expected coverage. We compare the size of seed set with other baseline algorithms by using both real and synthetic networks. Our algorithm consistently outperforms other methods. For certain cases, the sizes of seed sets returned by our algorithm are less than a hundredth of that returned by the baseline algorithms compared.

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Section: Discussionmentioning

confidence: 92%

Minimizing Seed Selection for Disseminating News with Probabilistic Coverage Guarantee

Lee

Hsu

2015

Proceedings of the 17th International Conference on Electronic Commerce 2015

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“…where q1, q2, q3, q4, q5 and q6 represent the optimal lag play an extensive role in political, social, cultural and economic transformations (Chierichetti, Lattanzi, & Panconesi, 2009;Doerr, Fouz, & Friedrich, 2012;Kwon, 2013). The negative impact of rumours and hearsay circulating in the current world of social networks has shaped a vast nature of public opinion and brought about ever-increasing social, cultural, political and economic crises, as well as waning social capital (Sun, Liu, He, & Du, 2013).…”

Section: Economic Growthmentioning

confidence: 99%

Social Networks, Exchange Rate Fluctuation, and Economic Growth: ARDL Approach

Ebrahimi

Alipour

Gholampour

et al. 2019

Tékhne

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This study aimed to investigate the effects of social network propaganda on exchange rate and also exchange rate fluctuations on Iran economic growth. This study uses annual data to analyse the long-run and short-run relationship between variables for the period of 1993–2018. Data were collected from the Central Bank of Iran. The autoregressive distributive lag (ARDL) method proposed by Pesaran, Shin, and Smith (2001) was used. The results of long-run analysis show that a 1% increase in negative propaganda of social media about the exchange rate leads to a 3.8% decline in long-run economic growth. Also, a 1% increase in exchange rate fluctuations results in a 3.5% decrease in economic growth. Research findings also indicate negative short-run impacts of social networks on the excitement of the foreign exchange market and, ultimately, on economic growth.

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“…A rumor is dispersed for achieving specific purpose, especially when a major public event occur and people do not have exact information and knowledge about the event [1][2][3]. The rapidly popularized online social networks (OSNs) greatly enhance the speed and enlarge the extent of rumor spreading [4,5]. However, most false rumors can inflict economic losses or disrupt social order [6].…”

Section: Introductionmentioning

confidence: 99%

An effective rumor-containing strategy

Pan

Yang

et al. 2018

Physica A: Statistical Mechanics and its Applications

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False rumors can lead to huge economic losses or/and social instability. Hence, mitigating the impact of bogus rumors is of primary importance. This paper focuses on the problem of how to suppress a false rumor by use of the truth. Based on a set of rational hypotheses and a novel rumor-truth mixed spreading model, the effectiveness and cost of a rumor-containing strategy are quantified, respectively. On this basis, the original problem is modeled as a constrained optimization problem (the RC model), in which the independent variable and the objective function represent a rumor-containing strategy and the effectiveness of a rumor-containing strategy, respectively. The goal of the optimization problem is to find the most effective rumor-containing strategy subject to a limited rumor-containing budget. Some optimal rumor-containing strategies are given by solving their respective RC models. The influence of different factors on the highest cost effectiveness of a RC model is illuminated through computer experiments. The results obtained are instructive to develop effective rumor-containing strategies. equation. Due to the exact modeling and the perfect accommodation of the network topology, this model characterizes the SIS epidemics more accurately as compared with all previous SIS models. Moreover, this model is analytically tractable, resulting in profound conclusions. In recent years, this individual-based modeling approach has been widely applied to areas such as epidemic spreading [42][43][44], malware propagation [45][46][47][48][49][50][51][52], cyber security [53][54][55][56][57], and message transmission [58]. To our knowledge, to date the rumor-containing problem has not yet been studied under individual-level rumor spreading models.Clarifying rumors by releasing truths is a common way to inhibit rumors [59,60]. In this context, every individual may choose to believe the rumor or believe the truth or be uncertain. This paper focuses on the problem of how to inhibit a false rumor by use of the truth. Based on a novel individual-level rumor-truth mixed spreading model, the effectiveness and cost of a rumor-containing strategy are quantified. Thereby, the original problem is modeled as a constrained optimization problem (the rumor-containing (RC) model), in which the independent variable and the objective function represent a rumor-containing strategy and the effectiveness of a rumor-containing strategy, respectively. The goal of the optimization problem is to find the most effective rumor-containing strategy subject to a limited rumor-containing budget. Some optimal rumor-containing strategies are given by solving their respective RC models. The influence of different factors on the highest cost effectiveness of a RC model is uncovered through computer simulations. These results are condusive to the design of practical rumor-containing strategies. To our knowledge, this is the first time the rumor-containing problem is treated in this way.The subsequent materials of this work are organized in this fashion: ...

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Experimental Analysis of Rumor Spreading in Social Networks

Cited by 19 publications

References 30 publications

Minimizing Seed Selection for Disseminating News with Probabilistic Coverage Guarantee

Minimizing Seed Selection for Disseminating News with Probabilistic Coverage Guarantee

Social Networks, Exchange Rate Fluctuation, and Economic Growth: ARDL Approach

An effective rumor-containing strategy

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