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A Congressional Twitter network dataset quantifying pairwise probability of influence.

Christian G Fink1, Nathan Omodt2, Sydney Zinnecker1

  • 1Gonzaga University Physics Department, Gonzaga University, 502 E Boone Ave Spokane, WA 99258, USA.

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Summary

This study introduces a social network dataset of the 117th United States Congress, detailing influence probabilities between members based on Twitter interactions. This network aids in understanding information diffusion in political social networks.

Keywords:
Independent Cascade ModelInformation diffusionSocial networkSusceptible-Infected-Recovered (SIR) modelTwitter network

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Area of Science:

  • Social Network Analysis
  • Political Science
  • Computational Social Science

Background:

  • Understanding political communication dynamics is crucial for analyzing legislative behavior.
  • Social media platforms like Twitter have become significant channels for political discourse and interaction.

Purpose of the Study:

  • To create a novel social network dataset of the 117th United States Congress.
  • To quantify "probabilities of influence" between members of Congress based on their Twitter activity.

Main Methods:

  • Collected interaction data (retweets, quote tweets, replies, mentions) from Twitter API V2 for members of the 117th Congress.
  • Constructed a directed, weighted network where edge weights represent empirically derived influence probabilities.
  • Normalized influence metrics by the number of tweets issued by each Congressperson.

Main Results:

  • A comprehensive dataset of pairwise "probabilities of influence" among Congress members was generated.
  • The network captures the directed nature of influence based on specific Twitter interactions.

Conclusions:

  • The developed dataset offers a unique resource for studying information diffusion and network structures within a political context.
  • This network can facilitate research into the flow of information and the dynamics of influence among legislators.