Opinion propagation in bounded medium-sized populations

Cuypere, Eline De; Turck, Koen De; Wittevrongel, Sabine; Fiems, Dieter

doi:10.1016/j.peva.2016.03.001

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…We simulate the rumor spread process on a Facebook sample graph taken from the SNAP project (Leskovec 2012), on a random graph, and on a series of synthetic Watts-Strogatz networks (Watts and Strogatz 1998). Like other studies in the literature on rumor spread online (Cuypere et al 2016), we also consider a mid-sized network (500 nodes). The intuition behind this is that opinions and rumors often spread within a particular online community which is not that large-for example, people contributing to an online forum, or people tweeting and re-tweeting some hashtag.…”

Section: Related Workmentioning

confidence: 99%

“…Like other studies in the literature on rumor spread online (Cuypere et al. 2016 ), we also consider a mid-sized network (500 nodes). The intuition behind this is that opinions and rumors often spread within a particular online community which is not that large—for example, people contributing to an online forum, or people tweeting and re-tweeting some hashtag.…”

Section: Related Workmentioning

confidence: 99%

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Bullshit in a network structure: the two-sided influence of self-generated signals

Tuchner

Gilboa-Freedman

2020

Soc. Netw. Anal. Min.

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In today's social network age, information flowing in networks does not derive solely from external sources; people in the network also independently generate signals. These self-generated signals may not be deliberate lies, but they may not bear any relationship with the truth, either. Following the philosopher Harry G. Frankfurt, we refer to such self-generated signals as bullshit. We present an information diffusion model that allows nodes which hold no value to spread information, capturing the diffusion of bullshit information. The presence of self-generated signals (i.e., bullshit) increases the amount of information available for transmission in the network. However, participants in the spread process respond to the existence of such self-generated information by receiving data from internal sources with caution. These two contradictory forces-the increase in information transmission on the one hand, and in suspicion on the other-result in a two-sided effect of bullshit on the total spread time. We first take a numerical approach, simulating our model on Watts-Strogatz networks and building a decision tree to characterize the effects of bullshit given different network structures. We find that increasing the rate of self-generated information may have either a monotonic or non-monotonic effect on the rumor spread time, depending on the network structure and rate of non-self-generated internal communications. Then, taking an analytical approach, we analyze the spread behavior for cliques, and identify the conditions for monotonic behavior in a 2-clique network.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Bullshit in a network structure: the two-sided influence of self-generated signals

Tuchner

Gilboa-Freedman

2020

Soc. Netw. Anal. Min.

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“…The approach is primarily algorithmic, with a focus on a recursion for calculating many terms in the series expansion, rather than on closed-form expressions for the first few terms. Such an algorithmic approach previously allowed for analysing queueing systems with coupled service [11,12], retrial queues with heterogeneous retrials [24], and epidemic processes in finite populations [25]. In order to improve convergence, we further rely on convergence acceleration techniques.…”

Section: Introductionmentioning

confidence: 99%

Performance of a Synchronisation Station with Abandonment

Fiems

2024

Mathematics

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The paper presents a Markovian queueing model for assessing the performance of synchronisation between stations in a production system. The system at hand consists of K distinct buffers, each buffer storing an item that is needed for the next production stage. Departures are immediate when all items are present. Due to the presence of multiple buffers, there is no reasonably fast way to calculate the stationary distribution of the Markov chain. Therefore, we focused on the series expansion of the stationary distribution in terms of the arrival rate. We provide a fast algorithm for calculating these terms. Comparing our results with stochastic simulation, we show that the expansion approach converges to the simulated values for a wide range of arrival rates.

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