Mass media impact on opinion evolution in biased digital environments: a bounded confidence model
Abstract People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users’ shared opinions and content from mainstream media sources. While online social networks have fostered information access and d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-39725-y |
| _version_ | 1797453234469601280 |
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| author | Valentina Pansanella Alina Sîrbu Janos Kertesz Giulio Rossetti |
| author_facet | Valentina Pansanella Alina Sîrbu Janos Kertesz Giulio Rossetti |
| author_sort | Valentina Pansanella |
| collection | DOAJ |
| description | Abstract People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users’ shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four different media landscapes and found that an open-minded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users’ biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda. |
| first_indexed | 2024-03-09T15:19:51Z |
| format | Article |
| id | doaj.art-b91106621d6947ffa7ed40169922129d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T15:19:51Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b91106621d6947ffa7ed40169922129d2023-11-26T12:54:08ZengNature PortfolioScientific Reports2045-23222023-09-0113111510.1038/s41598-023-39725-yMass media impact on opinion evolution in biased digital environments: a bounded confidence modelValentina Pansanella0Alina Sîrbu1Janos Kertesz2Giulio Rossetti3Faculty of Science, Scuola Normale SuperioreDepartment of Computer Science, University of PisaDepartment of Network and Data Science, Central European UniversityInstitute of Information Science and Technologies “A. Faedo” (ISTI), National Research Council (CNR)Abstract People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users’ shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the proliferation of polluted information and contents, which are argued to be among the co-causes of polarization/radicalization phenomena. Moreover, recommendation algorithms - intended to enhance platform usage - likely augment such phenomena, generating the so-called Algorithmic Bias. In this work, we study the effects of the combination of social influence and mass media influence on the dynamics of opinion evolution in a biased online environment, using a recent bounded confidence opinion dynamics model with algorithmic bias as a baseline and adding the possibility to interact with one or more media outlets, modeled as stubborn agents. We analyzed four different media landscapes and found that an open-minded population is more easily manipulated by external propaganda - moderate or extremist - while remaining undecided in a more balanced information environment. By reinforcing users’ biases, recommender systems appear to help avoid the complete manipulation of the population by external propaganda.https://doi.org/10.1038/s41598-023-39725-y |
| spellingShingle | Valentina Pansanella Alina Sîrbu Janos Kertesz Giulio Rossetti Mass media impact on opinion evolution in biased digital environments: a bounded confidence model Scientific Reports |
| title | Mass media impact on opinion evolution in biased digital environments: a bounded confidence model |
| title_full | Mass media impact on opinion evolution in biased digital environments: a bounded confidence model |
| title_fullStr | Mass media impact on opinion evolution in biased digital environments: a bounded confidence model |
| title_full_unstemmed | Mass media impact on opinion evolution in biased digital environments: a bounded confidence model |
| title_short | Mass media impact on opinion evolution in biased digital environments: a bounded confidence model |
| title_sort | mass media impact on opinion evolution in biased digital environments a bounded confidence model |
| url | https://doi.org/10.1038/s41598-023-39725-y |
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