SCCC

Scaled and Classified Congressional Communications

Michael Kistner, Michael Heseltine, Robert Alvarez, Maya Fitch, Lucas Lothamer and Elizabeth Simas

A dataset of over 7 million tweets, Facebook posts, and email newsletters sent by members of Congress between 2009 and 2022, classified by purpose and placed on a left-right partisan scale.

About the data

This website allows users to explore and download the data introduced in "Measuring Partisanship and Representation in Online Congressional Communications" (American Political Science Review 2026). The dataset covers the 111th through 117th Congresses (2009–2022) and contains messages sent by 958 Representatives and 120 Senators. Tweets were collected from the Twitter API, Facebook posts through CrowdTangle, and email newsletters from Lindsey Cormack's DCinbox archive.

Each message was coded into six categories drawn from Mayhew (1974), Fenno (1978) and Russell (2018): advertising, credit claiming, position taking, constituent service, negative partisanship and bipartisanship. Categories are not mutually exclusive. Labels come from BERTweet classifiers trained on roughly 43,000 hand-coded messages.

The partisan score places each message on a −1 (most Democratic) to +1 (most Republican) scale using a class affinity model fit separately for each Congress. Partisan extremity is the absolute value of that score.

Data are downloadable in three formats. First, data aggregated to the member and Congress level include message counts, partisan scores, and category proportions. Second, data aggregated at the month by party by chamber level include count variables of word and phrase usage (term frequency). Third, data at the message level includes identifiers (tweet ID, post URL, newsletter ID), partisan score, and categorizations. Message-level text is omitted due to platforms' terms of use, but researchers with API access to X or Facebook can rehydrate messages using identifiers. Newsletter text data can be obtained by emailing lindsey@dcinbox.com. For more questions about data access, please email one of the authors.

Contact: michael.heseltine@sociology.ox.ac.uk or mkistner@tamu.edu.

Explore

Explore by score, category, word or phrase, Congress, or member. Filter data with toggles below. Newsletter results are computed per sentence so category shares are comparable across platforms.

By category

By word or phrase

Monthly share of messages containing a word or phrase, by platform, for the current filters.

By Congress

By member

Download

Codebook
Dataset descriptions, variable definitions, classifier and scaling details.

Member-level tables

members.csv
One row per member and Congress: name, party, chamber, state and district, plus message counts, the share of messages in each category, and mean partisan score and extremity — pooled across platforms and separately for Twitter (Twt), Facebook (FB) and newsletters (NL).

Term frequency files

For each searchable term (used in minimum 50 messages), the number of messages containing it in each month, by party and chamber, one file per platform, plus monthly message totals for computing rates.

Message-level files

Each row is one tweet, one Facebook post, one newsletter or one newsletter sentence, with its platform identifier, date, member ICPSR ID, Congress, engagement counts (social media only), the eight classifier labels and the two partisanship scores. Newsletters were classified sentence by sentence: the newsletter files mark a category if any sentence carries it, while the sentence files, which the member-level tables and the explorer are built from, give the per-sentence labels.

Cite

If you use the data, please cite the article and the dataset.

Kistner, Michael, Michael Heseltine, Robert Alvarez, Maya Fitch, Lucas Lothamer, and Elizabeth Simas. 2026. "Measuring Partisanship and Representation in Online Congressional Communications." American Political Science Review, 1–18. doi:10.1017/S0003055426101841

Kistner, Michael, Michael Heseltine, Robert Alvarez, Maya Fitch, Lucas Lothamer, and Elizabeth Simas. 2026. "Replication Data for: Measuring Partisanship and Representation in Online Congressional Communications." Harvard Dataverse. doi:10.7910/DVN/19MIBB

@article{kistner2026sccc,
  title   = {Measuring Partisanship and Representation in Online Congressional Communications},
  author  = {Kistner, Michael and Heseltine, Michael and Alvarez, Robert and Fitch, Maya and Lothamer, Lucas and Simas, Elizabeth},
  journal = {American Political Science Review},
  year    = {2026},
  pages   = {1--18},
  doi     = {10.1017/S0003055426101841}
}

If you use the newsletter data, please also cite the DCinbox archive:

Cormack, Lindsey. 2017. "DCinbox—Capturing Every Congressional Constituent E-Newsletter from 2009 Onwards." The Legislative Scholar 2 (1): 27–34.

@article{cormack2017dcinbox,
  title={DCinbox—Capturing every congressional constituent e-newsletter from 2009 onwards},
  author={Cormack, Lindsey},
  journal={The Legislative Scholar},
  volume={2},
  number={1},
  pages={27--34},
  year={2017}
}