Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study
Misinformation has posed significant threats to all aspects of people’s lives. One of the most active areas of research in misinformation examines how individuals are misinformed. In this paper, we study how and to what extent agents are misinformed in an extended bounded confidence model, which con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/26/2/99 |
| _version_ | 1797298386753290240 |
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| author | Yujia Wu Peng Guo |
| author_facet | Yujia Wu Peng Guo |
| author_sort | Yujia Wu |
| collection | DOAJ |
| description | Misinformation has posed significant threats to all aspects of people’s lives. One of the most active areas of research in misinformation examines how individuals are misinformed. In this paper, we study how and to what extent agents are misinformed in an extended bounded confidence model, which consists of three parts: (i) online selective neighbors whose opinions differ from their own but not by more than a certain confidence level; (ii) offline neighbors, in a Watts–Strogatz small-world network, whom an agent has to communicate with even though their opinions are far different from their own; and (iii) a Bayesian analysis. Furthermore, we introduce two types of epistemically irresponsible agents: agents who hide their honest opinions and focus on disseminating misinformation and agents who ignore the messages received and follow the crowd mindlessly. Simulations show that, in an environment with only online selective neighbors, the misinforming is more successful with broader confidence intervals. Having offline neighbors contributes to being cautious of misinformation, while employing a Bayesian analysis helps in discovering the truth. Moreover, the agents who are only willing to listen to the majority, regardless of the truth, unwittingly help to bring about the success of misinformation attempts, and they themselves are, of course, misled to a greater extent. |
| first_indexed | 2024-03-07T22:34:07Z |
| format | Article |
| id | doaj.art-f69aa4f4cdfa46f38d818e99070444f9 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-07T22:34:07Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-f69aa4f4cdfa46f38d818e99070444f92024-02-23T15:15:36ZengMDPI AGEntropy1099-43002024-01-012629910.3390/e26020099Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation StudyYujia Wu0Peng Guo1School of Management, Northwestern Polytechnical University, Xi’an 710021, ChinaSchool of Management, Northwestern Polytechnical University, Xi’an 710021, ChinaMisinformation has posed significant threats to all aspects of people’s lives. One of the most active areas of research in misinformation examines how individuals are misinformed. In this paper, we study how and to what extent agents are misinformed in an extended bounded confidence model, which consists of three parts: (i) online selective neighbors whose opinions differ from their own but not by more than a certain confidence level; (ii) offline neighbors, in a Watts–Strogatz small-world network, whom an agent has to communicate with even though their opinions are far different from their own; and (iii) a Bayesian analysis. Furthermore, we introduce two types of epistemically irresponsible agents: agents who hide their honest opinions and focus on disseminating misinformation and agents who ignore the messages received and follow the crowd mindlessly. Simulations show that, in an environment with only online selective neighbors, the misinforming is more successful with broader confidence intervals. Having offline neighbors contributes to being cautious of misinformation, while employing a Bayesian analysis helps in discovering the truth. Moreover, the agents who are only willing to listen to the majority, regardless of the truth, unwittingly help to bring about the success of misinformation attempts, and they themselves are, of course, misled to a greater extent.https://www.mdpi.com/1099-4300/26/2/99misinformationbounded confidenceopinion dynamicssmall-world networksheterogeneity |
| spellingShingle | Yujia Wu Peng Guo Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study Entropy misinformation bounded confidence opinion dynamics small-world networks heterogeneity |
| title | Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study |
| title_full | Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study |
| title_fullStr | Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study |
| title_full_unstemmed | Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study |
| title_short | Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study |
| title_sort | modeling misinformation spread in a bounded confidence model a simulation study |
| topic | misinformation bounded confidence opinion dynamics small-world networks heterogeneity |
| url | https://www.mdpi.com/1099-4300/26/2/99 |
| work_keys_str_mv | AT yujiawu modelingmisinformationspreadinaboundedconfidencemodelasimulationstudy AT pengguo modelingmisinformationspreadinaboundedconfidencemodelasimulationstudy |