Preventing online disinformation propagation: Cost-effective dynamic budget allocation of refutation, media censorship, and social bot detection

Yi Wang; Shicheng Zhong; Guo Wang; Yi Wang; Shicheng Zhong; Guo Wang

doi:10.3934/mbe.2023584

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 7: 13113-13132. doi: 10.3934/mbe.2023584

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Preventing online disinformation propagation: Cost-effective dynamic budget allocation of refutation, media censorship, and social bot detection

1.
School of Big Data and Information Industry, Chongqing City Management College, Chongqing 400000, China
2.
Chongqing Longjin Technology Co., Ltd, Chongqing 400000, China
3.
College of Mechanical Engineering, Chongqing Wuyi Polytechinc College, Chongqing 400000, China

Academic Editor: Qingyi Zhu

Received: 29 March 2023 Revised: 15 May 2023 Accepted: 23 May 2023 Published: 06 June 2023

Disinformation refers to false rumors deliberately fabricated for certain political or economic conspiracies. So far, how to prevent online disinformation propagation is still a severe challenge. Refutation, media censorship, and social bot detection are three popular approaches to stopping disinformation, which aim to clarify facts, intercept the spread of existing disinformation, and quarantine the source of disinformation, respectively. In this paper, we study the collaboration of the above three countermeasures in defending disinformation. Specifically, considering an online social network, we study the most cost-effective dynamic budget allocation (DBA) strategy for the three methods to minimize the proportion of disinformation-supportive accounts on the network with the lowest expenditure. For convenience, we refer to the search for the optimal DBA strategy as the DBA problem. Our contributions are as follows. First, we propose a disinformation propagation model to characterize the effects of different DBA strategies on curbing disinformation. On this basis, we establish a trade-off model for DBA strategies and reduce the DBA problem to an optimal control model. Second, we derive an optimality system for the optimal control model and develop a heuristic numerical algorithm called the DBA algorithm to solve the optimality system. With the DBA algorithm, we can find possible optimal DBA strategies. Third, through numerical experiments, we estimate key model parameters, examine the obtained DBA strategy, and verify the effectiveness of the DBA algorithm. Results show that the DBA algorithm is effective.
- disinformation propagation,
- refutation,
- media censorship,
- social bot detection,
- collaborative dynamic budget allocation,
- optimal control
Citation: Yi Wang, Shicheng Zhong, Guo Wang. Preventing online disinformation propagation: Cost-effective dynamic budget allocation of refutation, media censorship, and social bot detection[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13113-13132. doi: 10.3934/mbe.2023584

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Abstract

Disinformation refers to false rumors deliberately fabricated for certain political or economic conspiracies. So far, how to prevent online disinformation propagation is still a severe challenge. Refutation, media censorship, and social bot detection are three popular approaches to stopping disinformation, which aim to clarify facts, intercept the spread of existing disinformation, and quarantine the source of disinformation, respectively. In this paper, we study the collaboration of the above three countermeasures in defending disinformation. Specifically, considering an online social network, we study the most cost-effective dynamic budget allocation (DBA) strategy for the three methods to minimize the proportion of disinformation-supportive accounts on the network with the lowest expenditure. For convenience, we refer to the search for the optimal DBA strategy as the DBA problem. Our contributions are as follows. First, we propose a disinformation propagation model to characterize the effects of different DBA strategies on curbing disinformation. On this basis, we establish a trade-off model for DBA strategies and reduce the DBA problem to an optimal control model. Second, we derive an optimality system for the optimal control model and develop a heuristic numerical algorithm called the DBA algorithm to solve the optimality system. With the DBA algorithm, we can find possible optimal DBA strategies. Third, through numerical experiments, we estimate key model parameters, examine the obtained DBA strategy, and verify the effectiveness of the DBA algorithm. Results show that the DBA algorithm is effective.

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