election officials

Abstract: Recent elections in the United States have seen online rumors become a regular feature of election discourse as voters and election officials work to navigate a sea of digitally spread claims and propaganda. For the uninitiated, these rumors can feel overwhelming and random, sowing doubt about the integrity and legitimacy of recent elections. In this paper, we reviewed and qualitatively coded a large dataset of 590 election rumors collected during the the 2020 and 2022 national elections. We found that there are tropes that recur time and time again in election rumors, highlighting how these rumors are not as random as they may first appear. We make two primary contributions in this work: first, we introduce a hierarchical framework that structures observed tropes in three tiers (Meta-category, Meso-trope, and Micro-trope). Second, combining the framework with our coded data, we identify a visible grammar of election rumors, highlighting how in the majority of cases a rumor contains an Actor, and Object, and an Action, with optional grammatical components of Suggested Remedies and Scene & Setting. The framework and grammar can be used by other researchers to act as the underlying information architecture of analyses examining similar rumors, enabling the rapid identification, analysis, and prediction of election rumors.

Abstract: We present Alaska's ranked-choice elections as a case study in Formally Verified Democracy, a program to establish election results through formally checked proofs. We formalize an interpretation of Alaska's tabulation rules in Lean, verify its implementation, and prove election-specific results. Across 115 ranked-choice contests in Alaska since 2022, we identify 75 ballot records across 10 contests that were counted differently from this interpretation by a program reproducing the published results, without changing any winners. We trace these differences to overvotes and duplicate rankings and examine their legal justification, showing how formal verification can expose consequential choices in translating election law into software.

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.

Abstract: The Single Transferable Vote (STV) is an algorithmic election rule. Round by round, a profile of ranked-choice ballots is reinterpreted to determine which decision to make next, and candidates are seated or eliminated until a full winner set emerges. This algorithmic nature makes STV theoretically more brittle than other election rules: uncertainty about an early round of the election might percolate irreversibly into the rest. For this reason, a generalized non-trivial Risk-Limiting Audit (RLA) framework has remained elusive for STV. Such a framework must concoct a set of null hypotheses, or assertions, whose rejection would bound the probability that the outcome of the election was incorrectly reported. We present audit graphs as a solution to design the assertions needed for RLAs of arbitrary STV elections, as well as quantitatively describe the uncertainty (or lack thereof) of their outcomes. These audit graphs explore election paths that are ``close'' to the recorded one by considering the alternative decisions the STV algorithm might have made if a small number of ballots were perturbed.