gsprint23/CongressionalTwitterNetwork: Data in Brief Article

Document Type

Dataset

Abstract

This repository stores the accompanying code and data for the weighted, bidirectional graph (henceforth referred to as a "Twitter Influence Network" graph) presented in the research papers 1. Fink et. al "A centrality measure for quantifying spread on weighted, directed networks" Physica A, 2023 (DOI link: https://doi.org/10.1016/j.physa.2023.129083) and 2. Fink et. al "A Congressional Twitter network dataset quantifying pairwise probability of influence" Data in Brief (https://doi.org/10.1016/j.dib.2023.109521 or https://repository.gonzaga.edu/physicsschol/2). This graph represents the how information flows in a network of US Congress members. Tweets from these members span the date range between February 9, 2022, and June 9, 2022. Information flow was modeled as "influence": every time Congressional members retweeted, quote-tweeted, replied to, or mentioned one another.

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DOI

https://doi.org/10.5281/zenodo.8253486

Publisher

Zenodo

Publication Date

8-16-2023

Disciplines

Computer Sciences | Data Science | Numerical Analysis and Scientific Computing

Comments

This dataset is shared under the MIT License. The zipped folder contains a README (.md) file, code files (.py), data files (.txt), and network data files (.json), along with license, use, and requirement information. 

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