congress_twitter — US Congress Twitter influence network (2022)

Description

A directed network of influence on Twitter among the members of the 117th United States Congress (House of Representatives and Senate), collected by C. G. Fink, G. Sprint and collaborators from the Twitter API. It is based on the tweets of the members between February 9 and June 9, 2022. Nodes are members of Congress, identified by their Twitter user names. An edge points from member A to member B if B retweeted, quote tweeted, replied to or mentioned A at least once (a mention is not counted again if it is also a reply, retweet or quote of the same member). The weight of the edge is the number of such interactions divided by the number of tweets of A in the period, an empirical estimate of the probability that a tweet of A is passed on by B.

The authors left out the members with fewer than 100 tweets in the period, and self-interactions. Only the normalized weights are given, not the numbers of interactions or tweets, and the tweets themselves can only be retrieved (rehydrated) through the Twitter API, so the network cannot be recomputed without that threshold.

The network has the following properties:

(Node) name: the Twitter user name of the member.
(Edge) weight: the fraction of the tweets of the source member that the target member retweeted, quote tweeted, replied to or mentioned.

Tags

Social Online Political Weighted

Added

2026-10-27

Citation

C. G. Fink, N. Omodt, S. Zinnecker, and G. Sprint, "A Congressional Twitter network dataset quantifying pairwise probability of influence." Data in Brief 50, 109521 (2023). 10.1016/j.dib.2023.109521 Sci-Hub
C. G. Fink, K. Fullin, G. Gutierrez, N. Omodt, S. Zinnecker, G. Sprint, and S. McCulloch, "A centrality measure for quantifying spread on weighted, directed networks." Physica A 626, 129083 (2023). 10.1016/j.physa.2023.129083 Sci-Hub

Upstream URL OK

https://snap.stanford.edu/data/congress-twitter.html
https://github.com/gsprint23/CongressionalTwitterNetwork

Upstream license

MIT License, https://github.com/gsprint23/CongressionalTwitterNetwork/blob/205ca40d973e13944051e1e44240d005a9b1a1bc/LICENSE.txt

Networks

Tip: hover over a column header for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
congress_twitter	475	13,289	27.98	34.79	55.93	4.97	-0.07	0.29	4	1.00	Directed	Unipartite	name	weight	40 KiB	79 KiB	77 KiB	72 KiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.