The impact of group propagation on rumor spreading in mobile social networks

Sahafizadeh, Ebrahim; Ladani, Behrouz Tork

doi:10.1016/j.physa.2018.04.038

Cited by 58 publications

(12 citation statements)

References 18 publications

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“…Another interesting finding is that when the initial emotion of s is negative and neutral, more individuals tend to be positive, while when the initial emotion of s is positive, more individuals tend to be immune. Finally, the convergence time of the large-scale network contagion process is long, and its final stabilization effect is relatively poor 37 .…”

Section: Simulation Resultsmentioning

confidence: 99%

A model and simulation of the emotional contagion of netizens in the process of rumor refutation

Zeng

Zhu²

2019

Sci Rep

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The emotional contagion of netizens is an important factor that accelerates the spread of rumors, and it is also key to the effectiveness of rumor refutation. Based on the existing emotional model, we improved the method for calculating the emotional value and the transformation rules to simulate how the infection transforms individual emotion to group emotion during rumor refutation. The results show that the cycle and trend of netizen emotional change vary by period, but the final distribution structure presents a relatively stable state. The factors that affect the emotional changes of netizens are mainly objective and subjective aspects, both of which can promote the evolution of emotional contagion. The objective aspect depends on the speed and credibility of the rumor, and the subjective aspect depends on the degree of intimacy between netizens. After rumor refutation, emotions generally change from negative emotions to positive or immune emotions.

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Section: Simulation Resultsmentioning

confidence: 99%

A model and simulation of the emotional contagion of netizens in the process of rumor refutation

Zeng

Zhu²

2019

Sci Rep

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“…Zhou et al [19] find that positive energy information would have a great contribution to society when information bombing takes place. Sahafizadeh and Tork Ladani [20] extend the SIR information propagation model and conclude that rumor-spreading behavior in these networks do not make a significant difference if there is rumor propagation in groups. Fibich [21] believes that a smallworlds structure has a negligible effect on the diffusion based on Bass-SIR model.…”

Section: Literature Reviewmentioning

confidence: 95%

The Influence of Investor Emotion on the Stock Market: Evidence from an Infectious Disease Model

Chen

Zhu

2021

Discrete Dynamics in Nature and Society

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In March 2020, four consecutive circuit breakers in the US stock market underscored the impact of investor sentiment on the stock market. With the development of technology, public opinion and other information now spread easily through social media and other channels, indirectly affecting investor sentiment. This makes it important to understand the underlying dynamics of such situations to help manage the market impact of such events going forward. To that end, we analyze investor sentiment, investor structures, and the capital market fuse mechanism using infectious disease dynamics. We use an extension of the SIR (susceptible, infectious, and recovered) model, called the dynamic SIRS model (where individuals return to a susceptible state), to simulate the impact of investor sentiment on the stock market. Accordingly, we study the circuit breakers in the US stock market and the simulation results of the model to analyze the fuse mechanism process in China that triggers a pause in the market based on volatile trading. The results of our study show that when the influence rate of investor mutual communication increases or when the emotional calm rate decreases, investor emotions will start to diffuse, leading to an increase in the probability of either a serious stampede or zealous overbuying in the stock market. At the same time, the trading frequency of investors and the ratio of investors in both buying and selling directions will have a certain formal impact on the direction of the stock market, with the final impact determined by the ratio of normal investors to emotional investors. When emotional investors dominate the market, their emotions are diffused throughout. Our study provides the reference for relevant agencies to monitor and improve the stock market fuse mechanism in the future.

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“…Using the SIR convention, individuals who adopt this disinformation and react to it directly by consuming more power can be classified as “Infected.” Alternatively, users who are not influenced by this disinformation, for any reason, can be classified as “Removed.” And users who have not received notification yet can be classified as “Susceptible.” This classification of categories allows us to model, quantify, and predict their power usage during disinformation dissemination. There is a rich literature that formulates the phenomenon of transition between categorical labels with SIR models that employ a system of differential equations based on mean field theory or agent-based models that allow us to simulate the transmission of disinformation among autonomous agents in a flexible microscale manner 19 – 21 .…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Protecting infrastructure performance from disinformation attacks

Jamalzadeh

Barker

González

et al. 2022

Sci Rep

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Disinformation campaigns are prevalent, affecting vaccination coverage, creating uncertainty in election results, and causing supply chain disruptions, among others. Unfortunately, the problems of misinformation and disinformation are exacerbated due to the wide availability of online platforms and social networks. Naturally, these emerging disinformation networks could lead users to engage with critical infrastructure systems in harmful ways, leading to broader adverse impacts. One such example involves the spread of false pricing information, which causes drastic and sudden changes in user commodity consumption behavior, leading to shortages. Given this, it is critical to address the following related questions: (i) How can we monitor the evolution of disinformation dissemination and its projected impacts on commodity consumption? (ii) What effects do the mitigation efforts of human intermediaries have on the performance of the infrastructure network subject to disinformation campaigns? (iii) How can we manage infrastructure network operations and counter disinformation in concert to avoid shortages and satisfy user demands? To answer these questions, we develop a hybrid approach that integrates an epidemiological model of disinformation spread (based on a susceptible-infectious-recovered model, or SIR) with an efficient mixed-integer programming optimization model for infrastructure network performance. The goal of the optimization model is to determine the best protection and response actions against disinformation to minimize the general shortage of commodities at different nodes over time. The proposed model is illustrated with a case study involving a subset of the western US interconnection grid located in Los Angeles County in California.

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The impact of group propagation on rumor spreading in mobile social networks

Cited by 58 publications

References 18 publications

A model and simulation of the emotional contagion of netizens in the process of rumor refutation

A model and simulation of the emotional contagion of netizens in the process of rumor refutation

The Influence of Investor Emotion on the Stock Market: Evidence from an Infectious Disease Model

Protecting infrastructure performance from disinformation attacks

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