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Twitter (X) data list for U.S. state legislators (10-state subset)

Source: R/data.R
state_twitter.Rd

This data object is data derived from the Twitter (X) interactions between U.S. state legislators, which is a subset of the data analyzed in Fritz et al. (2025).' The data is filtered to include only legislators from 10 states (NY, CA, TX, FL, IL, PA, OH, GA, NC, MI) and is further subset to the largest connected component based on mention or retweet activity.

This object contains the main iglm.data object and 5 pre-computed dyadic covariates.

Usage

data(state_twitter)

Format

A list object containing 6 components. Let N be the number of legislators in the filtered 10-state subset.

iglm.data

A iglm.data object (which is also a list) parameterized as follows:

  • x_attribute: A binary numeric vector of length N. Value is 1 if the legislator's party is 'Republican', 0 otherwise.

  • y_attribute: A Poisson numeric vector of length N. Represents the count of hatespeech incidents (actors_data$number_hatespeech) for each legislator.

  • z_network: A directed edgelist (2-column matrix) of size n_edges x 2. A tie (i, j) exists if legislator i either mentioned or retweeted legislator j.

  • neighborhood: A directed edgelist (2-column matrix). Represents the follower network, where a tie (i, j) exists if legislator i follows legislator j. Self-loops (diagonal) are included.

match_gender

An N x N matrix. matrix[i, j] = 1 if legislator i and legislator j have the same gender, 0 otherwise.

match_race

An N x N matrix. matrix[i, j] = 1 if legislator i and legislator j have the same race, 0 otherwise.

match_state

An N x N matrix. matrix[i, j] = 1 if legislator i and legislator j are from the same state, 0 otherwise.

white_attribute

A 1 x N matrix (a row vector). matrix[1, i] = 1 if legislator i is 'White', 0 otherwise.

gender_attribute

A 1 x N matrix (a row vector). matrix[1, i] = 1 if legislator i is 'female', 0 otherwise.

References

Gopal, Kim, Nakka, Boehmke, Harden, Desmarais. The National Network of U.S. State Legislators on Twitter. Political Science Research & Methods, Forthcoming.

Kim, Nakka, Gopal, Desmarais,Mancinelli, Harden, Ko, and Boehmke (2022). Attention to the COVID-19 pandemic on Twitter: Partisan differences among U.S. state legislators. Legislative Studies Quarterly 47, 1023–1041.

Fritz, C., Schweinberger, M. , Bhadra S., and D. R. Hunter (2025). A Regression Framework for Studying Relationships among Attributes under Network Interference. Journal of the American Statistical Association, to appear.