Modelling rapid online cultural transmission: evaluating neutral models on Twitter data with approximate Bayesian computation
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- dc.contributor.author Carrignon, Simon, 1987-
- dc.contributor.author Bentley, R. Alexander
- dc.contributor.author Ruck, Damia
- dc.date.accessioned 2023-01-24T09:43:47Z
- dc.date.available 2023-01-24T09:43:47Z
- dc.date.issued 2019
- dc.description Supplemental material file: online appendix
- dc.description.abstract As social media technologies alter the variation, transmission and sorting of online information, short-term cultural evolution is transformed. In these media contexts, cultural evolution is an intra-generational process with much ‘horizontal’ transmission. As a pertinent case study, here we test variations of culture-evolutionary neutral models on recently-available Twitter data documenting the spread of true and false information. Using Approximate Bayesian Computation to resolve the full joint probability distribution of models with different social learning biases, emphasizing context versus content, we explore the dynamics of online information cascades: Are they driven by the intrinsic content of the message, or the extrinsic value (e.g., as a social badge) whose intrinsic value is arbitrary? Despite the obvious relevance of specific learning biases at the individual level, our tests at the online population scale indicate that unbiased learning model performs better at modelling information cascades whether true or false.
- dc.description.sponsorship Funding for this work was provided by the ERC Advanced Grant EPNet (340828). Computational power was made available by the Barcelona Supercomputing Center (BSC). We thank the National Institute for Mathematical and Biological Sciences, University of Tennessee, for support to SC in summer 2018 and the Severo Ochoa Mobility Program for funding his stay. DJR was supported by grants from the University of Tennessee and the National Science Foundation (DataONE project).
- dc.format.mimetype application/pdf
- dc.identifier.citation Carrignon S, Bentley RA, Ruck D. Modelling rapid online cultural transmission: evaluating neutral models on Twitter data with approximate Bayesian computation. Palgrave Commun. 2019 Jul 30;5:83. DOI: 10.1057/s41599-019-0295-9
- dc.identifier.doi http://dx.doi.org/10.1057/s41599-019-0295-9
- dc.identifier.issn 2055-1045
- dc.identifier.uri http://hdl.handle.net/10230/55420
- dc.language.iso eng
- dc.publisher Nature Research
- dc.relation.ispartof Palgrave Communications. 2019 Jul 30;5:83
- dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/340828
- dc.rights This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.other Xarxes socials
- dc.subject.other Tecnologia de la informació
- dc.subject.other Cultura
- dc.subject.other Comunicació i cultura
- dc.subject.other Difusió cultural
- dc.title Modelling rapid online cultural transmission: evaluating neutral models on Twitter data with approximate Bayesian computation
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion