A Congressional Twitter network dataset quantifying pairwise probability of influence

We present a social network dataset based on interactions between members of the 117(th) 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 a...

Descripción completa

Detalles Bibliográficos
Autores principales: Fink, Christian G., Omodt, Nathan, Zinnecker, Sydney, Sprint, Gina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Data Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493874/
https://www.ncbi.nlm.nih.gov/pubmed/37701709
http://dx.doi.org/10.1016/j.dib.2023.109521
Descripción
Sumario:We present a social network dataset based on interactions between members of the 117(th) 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.

Ejemplares similares