A novel method for solving the multi-commodity flow problem on evolving networks

Fan, Huilong; Sun, Chongxiang; Long, Jun; Wang, Shangpeng; Zeng, Fei

doi:10.1016/j.comnet.2024.110451

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…The third contribution is to introduce a mathematical model to minimize the response time to COVID-19 misinformation spread on social networks and evaluate metrics that would reduce the agents' time to respond to any new misinformation spread in networks. The fourth contribution is that the model implements a multi-commodity network flow problem [25], [26] to simulate the misinformation spread between communities on social networks. The fifth contribution is that the model utilizes the information diffusion delay factor to enhance the agents' performance.…”

Section: Introductionmentioning

confidence: 99%

Reducing COVID-19 Misinformation Spread by Introducing Information Diffusion Delay Using Agent-based Modeling

Alassad,

Agarwal

2024

Preprint

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With the explosive growth of the Coronavirus Pandemic (COVID-19), misinformation on social media has developed into a global phenomenon with widespread and detrimental societal effects. Despite recent progress and efforts in detecting COVID-19 misinformation on social media networks, this task remains challenging due to the complexity, diversity, multi-modality, and high costs of fact-checking or annotation. In this research, we introduce a systematic and multidisciplinary agent-based modeling approach to limit the spread of COVID-19 misinformation and interpret the dynamic actions of users and communities in evolutionary online (or offline) social media networks. Our model was applied to a Twitter network associated with an armed protest demonstration against the COVID-19 lockdown in Michigan state in May 2020. We implemented a one-median problem to categorize the Twitter network into six key communities (nodes) and identified information exchange (links) within the network. We measured the response time to COVID-19 misinformation spread in the network and employed a cybernetic organizational method to monitor the Twitter network. The overall misinformation mitigation strategy was evaluated, and agents were allocated to interact with the network based on the measured response time and feedback. The proposed model prioritized the communities based on the agents’ response times at the operational level. It then optimized agent allocation to limit the spread of COVID-19 related misinformation from different communities, improved the information diffusion delay threshold to up to 3 minutes, and ultimately enhanced the mitigation process to reduce misinformation spread across the entire network.

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Section: Introductionmentioning

confidence: 99%