Document Type
Article
Publication Title
Data in Brief
Abstract
We present a social network dataset based on interactions between members of the 117th United States Congress between Feb. 9, 2022, and June 9, 2022. The dataset takes the form of a directed, weighted network in which the edge weights are empirically obtained “probabilities of influence” between all pairs of Congresspeople. Twitter's application programming interface (API) V2 was used to determine the number of times each member of Congress retweeted, quote tweeted, replied to, or mentioned other Congressional members, and the probability of influence was found by normalizing the summed influence by the number of tweets issued by each Congressperson. This network may be of particular interest to the study of information diffusion within social networks.
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DOI
https://doi.org/10.1016/j.dib.2023.109521
Volume
50
Publication Date
10-2023
Keywords
Social network, Twitter network, Information diffusion, Independent Cascade Model, Susceptible-Infected-Recovered (SIR) model
Disciplines
Computer Sciences | Data Science | Physics
ISSN
2352-3409
Recommended Citation
Fink, C. G., Omodt, N., Zinnecker, S., & Sprint, G. (2023). A Congressional Twitter network dataset quantifying pairwise probability of influence. Data in Brief, 50, 109521. https://doi.org/10.1016/j.dib.2023.109521
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Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Computer Sciences Commons, Data Science Commons, Physics Commons
Comments
This article describes the development of a dataset used for the following article: Fink, Christian G., Kelly Fullin, Guillermo Gutierrez, et al. 2023. “A Centrality Measure for Quantifying Spread on Weighted, Directed Networks.” Physica A: Statistical Mechanics and Its Applications 626 (September): 129083. https://doi.org/10.1016/j.physa.2023.129083.
The dataset can be found hosted on the Gonzaga IR as well as Zenodo and GitHub.