A Congressional Twitter network dataset quantifying pairwise probability of influence
Christian G. Fink, Nathan Omodt, Sydney Zinnecker, Gina Sprint
Abstract
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.
Topics & Concepts
Pairwise comparisonSocial network (sociolinguistics)Computer scienceSocial network analysisEnhanced Data Rates for GSM EvolutionSocial mediaData miningInformation retrievalWorld Wide WebArtificial intelligenceComplex Network Analysis TechniquesOpinion Dynamics and Social InfluenceSocial Media and Politics