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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.
Data in Brief
|September 13, 2023
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.
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.
Keywords:
Independent Cascade ModelInformation diffusionSocial networkSusceptible-Infected-Recovered (SIR) modelTwitter networkMore Related Videos
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