Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow
Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of com...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-02-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/22/3/265 |
| _version_ | 1798039347114541056 |
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| author | Tyson Pond Saranzaya Magsarjav Tobin South Lewis Mitchell James P. Bagrow |
| author_facet | Tyson Pond Saranzaya Magsarjav Tobin South Lewis Mitchell James P. Bagrow |
| author_sort | Tyson Pond |
| collection | DOAJ |
| description | Contagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior. |
| first_indexed | 2024-04-11T21:52:38Z |
| format | Article |
| id | doaj.art-e24c1a09a5da47318ad1f81f9a20680e |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T21:52:38Z |
| publishDate | 2020-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e24c1a09a5da47318ad1f81f9a20680e2022-12-22T04:01:12ZengMDPI AGEntropy1099-43002020-02-0122326510.3390/e22030265e22030265Complex Contagion Features without Social Reinforcement in a Model of Social Information FlowTyson Pond0Saranzaya Magsarjav1Tobin South2Lewis Mitchell3James P. Bagrow4Department of Mathematics & Statistics, University of Vermont, Burlington, VT 05405, USASchool of Mathematical Sciences, The University of Adelaide, Adelaide SA 5005, AustraliaSchool of Mathematical Sciences, The University of Adelaide, Adelaide SA 5005, AustraliaSchool of Mathematical Sciences, The University of Adelaide, Adelaide SA 5005, AustraliaDepartment of Mathematics & Statistics, University of Vermont, Burlington, VT 05405, USAContagion models are a primary lens through which we understand the spread of information over social networks. However, simple contagion models cannot reproduce the complex features observed in real-world data, leading to research on more complicated complex contagion models. A noted feature of complex contagion is social reinforcement that individuals require multiple exposures to information before they begin to spread it themselves. Here we show that the quoter model, a model of the social flow of written information over a network, displays features of complex contagion, including the weakness of long ties and that increased density inhibits rather than promotes information flow. Interestingly, the quoter model exhibits these features despite having no explicit social reinforcement mechanism, unlike complex contagion models. Our results highlight the need to complement contagion models with an information-theoretic view of information spreading to better understand how network properties affect information flow and what are the most necessary ingredients when modeling social behavior.https://www.mdpi.com/1099-4300/22/3/265online social networkssocial mediainformation spreadinginformation diffusioncross-entropy |
| spellingShingle | Tyson Pond Saranzaya Magsarjav Tobin South Lewis Mitchell James P. Bagrow Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow Entropy online social networks social media information spreading information diffusion cross-entropy |
| title | Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow |
| title_full | Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow |
| title_fullStr | Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow |
| title_full_unstemmed | Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow |
| title_short | Complex Contagion Features without Social Reinforcement in a Model of Social Information Flow |
| title_sort | complex contagion features without social reinforcement in a model of social information flow |
| topic | online social networks social media information spreading information diffusion cross-entropy |
| url | https://www.mdpi.com/1099-4300/22/3/265 |
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