A Congressional Twitter network dataset quantifying pairwise probability of influence

Christian G Fink; Nathan Omodt; Sydney Zinnecker; Gina Sprint

doi:10.1016/j.dib.2023.109521

A Congressional Twitter network dataset quantifying pairwise probability of influence

Data Brief. 2023 Aug 28:50:109521. doi: 10.1016/j.dib.2023.109521. eCollection 2023 Oct.

Authors

Christian G Fink¹, Nathan Omodt², Sydney Zinnecker¹, Gina Sprint³

Affiliations

¹ Gonzaga University Physics Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA.
² Gonzaga University Mechanical Engineering Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA.
³ Gonzaga University Computer Science Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA.

Abstract

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.

Keywords: Independent Cascade Model; Information diffusion; Social network; Susceptible-Infected-Recovered (SIR) model; Twitter network.