cs.CLOct 26, 2026

Frankly Speaking: The Company Election Integrity Keeps in Congressional Communication

Authors: Michael A. Robinette, Christina Barsky, Amanda D. Clark, Helen Flannery, Juniper Lovato

Organizations: Vermont Complex Systems Institute, University of Vermont, Burlington, 05405, U.S.A. · Community Development and Applied Economics, University of Vermont, Burlington, 05405, U.S.A. · Department of Computer Science, University of Vermont, Burlington, 05405, U.S.A. · Complexity Science Hub, Vienna, Austria

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Abstract

Election integrity is a shared commitment in American politics, but Democrats and Republicans in Congress increasingly mean different things by it. Analyzing 10,420 congressional e-newsletters sent from 2010 to 2025, we examine how the two parties invoke election terms such as integrity,'' security,'' and fraud.'' Using diachronic word embeddings, which track semantic change through how words are used in context, we measure how differently each party uses each term, how much each party shifts, and whether they share nearest semantic neighbors. We show that members of Congress do not need different words to tell different stories about elections. They need only place those same words in different company. Across nearly every term and year, the two parties surround election-integrity vocabulary with entirely different words. That divergence is easy to miss, because most election vocabulary is no more divergent than other words used at roughly the same frequency, and neither party's usage shifted more than the other's. Only a subset of terms grew more divergent, most notably certification,'' while rigged'' and stolen'' entered election discourse after 2020 and polarized there. By 2024, Democrats surround voter,'' election,'' and integrity'' with the language of access and administration. Republicans surround the same terms with citizenship verification,'' noncitizen voting,'' and border security.'' For one party, integrity means protecting eligible voters' ability to cast a ballot. For the other, it means protecting the electoral process from illegitimate ballots. Election officials, who must administer a single system under both definitions, inherit this tension.

Figures & tables

Figure 1: Cross-party divergence over time. Each panel shows one target term’s aligned cosine distance between the two parties’ vectors by year. The dashed line is the noise floor (median aligned distance of the 100 frequency-matched shared words). A year is absent when a term is not mentioned at least five times.
Figure 2: Cross-party divergence percentile over time. Each panel shows a term’s divergence percentile among the 100 frequency-matched shared words that year; a term at 90 is more divergent than 90% of comparable words. Dotted lines indicate a term fell below the five-occurrence minimum. Panels are ordered by the change shown beside each term (mean of last three observed years minus mean of first three).
Figure 3: Term frequency by party over time. Term frequency by party and year (blue, Democratic; red, Republican), with each party’s total shown beside the term.
Figure 4: Within-party semantic shift over time. Each point is the cosine distance between a party’s vectors for year tt and t+1t+1 (blue, Democratic; red, Republican); mean shift per party is shown beside the term.
Figure 5: Semantic neighborhood overlap. Each panel tracks one target term’s Jaccard similarity (ranges from 0-1, where 1 is identical neighbors) over time across parties and within each individual party. Black: cross-party Jaccard similarity of top-10 neighbor sets. Red and blue: within-party split-half baselines. Cross-party overlap below the lower baseline indicates meaningfully non-overlapping neighborhoods.
Figure 6: Top 10 nearest semantic neighbors in 2021 for (a) “election” and (b) “integrity”. The words each party uses most like the target term. Similarity decreases clockwise; line thickness indicates closeness. Purple marks neighbors shared by both parties.
Figure 7: Top 10 nearest semantic neighbors in 2024 for (a) “election” and (b) “integrity”.
Figure 8: Top 10 nearest semantic neighbors in 2020 for “voter”.
Figure 9: Top 10 nearest semantic neighbors in 2024 for “voter”.
Appendix figures & tables3 assets

Supplementary material from the paper’s appendix.

Appendix

TierMatch ruleSemanticDocuments
1Mentions any of 29 core election integrity terms—3,451 (33.1%)
2Mentions any of 41 contextual or administrative terms and clears the similarity threshold>0.3>0.36,047 (58.0%)
3Clears the high similarity threshold, no keyword required>0.5>0.50 925 0 (8.9%)
Total10,420 (100%)
Appendix
Table S1: Multi-tier filter used to construct the study corpus. Tiers are applied in order and are mutually exclusive: a document is assigned to the first tier whose rule it satisfies. Keyword matching is case-insensitive and unanchored, so a listed term matches within a longer word or phrase (e.g., “certify” matches “certifying”). Full term lists appear in Table S2 ; query templates in Table S3 . Tier counts sum to 10,423; three duplicates were removed, giving the final 10,420. Percentages in the final column are shares of the 10,420-document filtered corpus.
Tier 1 (29 terms)
election integrityelectoral integrity
election fraudelectoral fraud
election crimeselection integrity unit
voter fraudvoter integrity
vote integrityvote accuracy
election securityillegal voting
Appendix
Table S2: Filter term lexicon. Terms were supplied by election integrity subject matter experts on the research team. All Tier 2 terms are matched as a single pooled set.
#Query
1concerns about election integrity and voter fraud
2securing elections and preventing illegal voting and ballot harvesting
3election security and ballot verification
4voter identification and registration fraud
5noncitizen voting and proof-of-citizenship requirements to register and vote
6rigged elections and stolen votes
Appendix
Table S3: Semantic query templates. Each document was encoded with Sentence-BERT (all-mpnet-base-v2) and scored by its maximum cosine similarity across these 19 queries.
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