Influence Networks: Bayesian Modeling and Diffusion
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929472829128704 |
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| author | Sánchez-Gutiérrez, Samuel Sosa, Juan Luque, Carolina |
| author_facet | Sánchez-Gutiérrez, Samuel Sosa, Juan Luque, Carolina |
| contents | In this article, we make an innovative adaptation of a Bayesian latent space model based on projections in a novel way to analyze influence networks. By appropriately reparameterizing the model, we establish a formal metric for quantifying each individual's influencing capacity and estimating their latent position embedded in a social space. This modeling approach introduces a novel mechanism for fully characterizing the diffusion of an idea based on the estimated latent characteristics. It assumes that each individual takes the following states: Unknown, undecided, supporting, or rejecting an idea. This approach is demonstrated using a influence network from Twitter (now $\mathbb{X}$) related to the 2022 Tax Reform in Colombia. An exhaustive simulation exercise is also performed to evaluate the proposed diffusion process. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2408_13606 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Influence Networks: Bayesian Modeling and Diffusion Sánchez-Gutiérrez, Samuel Sosa, Juan Luque, Carolina Methodology Applications In this article, we make an innovative adaptation of a Bayesian latent space model based on projections in a novel way to analyze influence networks. By appropriately reparameterizing the model, we establish a formal metric for quantifying each individual's influencing capacity and estimating their latent position embedded in a social space. This modeling approach introduces a novel mechanism for fully characterizing the diffusion of an idea based on the estimated latent characteristics. It assumes that each individual takes the following states: Unknown, undecided, supporting, or rejecting an idea. This approach is demonstrated using a influence network from Twitter (now $\mathbb{X}$) related to the 2022 Tax Reform in Colombia. An exhaustive simulation exercise is also performed to evaluate the proposed diffusion process. |
| title | Influence Networks: Bayesian Modeling and Diffusion |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2408.13606 |