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
_version_ 1785104562822053888
author Fink, Christian G.
Omodt, Nathan
Zinnecker, Sydney
Sprint, Gina
author_facet Fink, Christian G.
Omodt, Nathan
Zinnecker, Sydney
Sprint, Gina
author_sort Fink, Christian G.
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10493874
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-104938742023-09-12 A Congressional Twitter network dataset quantifying pairwise probability of influence Fink, Christian G. Omodt, Nathan Zinnecker, Sydney Sprint, Gina Data Brief Data Article 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. Elsevier 2023-08-28 /pmc/articles/PMC10493874/ /pubmed/37701709 http://dx.doi.org/10.1016/j.dib.2023.109521 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Fink, Christian G.
Omodt, Nathan
Zinnecker, Sydney
Sprint, Gina
A Congressional Twitter network dataset quantifying pairwise probability of influence
title A Congressional Twitter network dataset quantifying pairwise probability of influence
title_full A Congressional Twitter network dataset quantifying pairwise probability of influence
title_fullStr A Congressional Twitter network dataset quantifying pairwise probability of influence
title_full_unstemmed A Congressional Twitter network dataset quantifying pairwise probability of influence
title_short A Congressional Twitter network dataset quantifying pairwise probability of influence
title_sort congressional twitter network dataset quantifying pairwise probability of influence
topic Data Article
url 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
work_keys_str_mv AT finkchristiang acongressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence
AT omodtnathan acongressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence
AT zinneckersydney acongressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence
AT sprintgina acongressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence
AT finkchristiang congressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence
AT omodtnathan congressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence
AT zinneckersydney congressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence
AT sprintgina congressionaltwitternetworkdatasetquantifyingpairwiseprobabilityofinfluence

Ejemplares similares