Do Voters Get the Information They Want? Understanding Authentic Voter FAQs in the US and How to Improve for Informed Electoral Participation

TL;DR

The paper addresses the lack of a national, comprehensive voter FAQ resource by constructing the first 50-state Voter FAQ dataset and introducing FAQ Information Quality Scores (FIQS) to quantify readability, summarization, sentiment, and topic coverage. It applies standardized NLP techniques (readability metrics, extractive/abstractive summarization, LDA topic modeling, and VADER sentiment) to analyze FAQs from SECs and LWV, revealing state-level leaders and laggards and proposing practical guidelines for improving the information ecosystem. The authors also explore state specificity of questions, promptability of LLMs (via fine-tuned Llama-3.1-8B), and provide a structured blueprint for how states can enhance accessibility, accuracy, and neutrality of election information. The work offers a foundation for developing decision-support tools and encourages broader data sourcing to strengthen informed electoral participation in the United States.

Abstract

Accurate information is crucial for democracy as it empowers voters to make informed decisions about their representatives and keeping them accountable. In the US, state election commissions (SECs), often required by law, are the primary providers of Frequently Asked Questions (FAQs) to voters, and secondary sources like non-profits such as League of Women Voters (LWV) try to complement their information shortfall. However, surprisingly, to the best of our knowledge, there is neither a single source with comprehensive FAQs nor a study analyzing the data at national level to identify current practices and ways to improve the status quo. This paper addresses it by providing the {\bf first dataset on Voter FAQs covering all the US states}. Second, we introduce metrics for FAQ information quality (FIQ) with respect to questions, answers, and answers to corresponding questions. Third, we use FIQs to analyze US FAQs to identify leading, mainstream and lagging content practices and corresponding states. Finally, we identify what states across the spectrum can do to improve FAQ quality and thus, the overall information ecosystem. Across all 50 U.S. states, 12% were identified as leaders and 8% as laggards for FIQS\textsubscript{voter}, while 14% were leaders and 12% laggards for FIQS\textsubscript{developer}.

Figures (14)

• Figure 1: A real-world example of Voter FAQ. Scores of content quality are (FIQSvoter, FIQSdeveloper) - MA (0.41, 0.38); CA (0.7, 0.7); GA (0.13, 0.18).
• Figure 2: US states leading and lagging in voter FAQ content quality, as assessed using cut-off of one standard deviation from mean on the metric (i.e., $\geq$ ($\mu$$\pm \sigma$); $\leq$ ($\mu$$\pm \sigma$)). We call them leaders and laggards, respectively.
• Figure 3: Scatter Plot of Generic vs Specific Questions Across States. This scatter plot illustrates the distribution of generic and specific questions across the QA datasets of all 50 US states. Generic questions, which address fundamental aspects of the voting process, are plotted against specific questions, which localize information to state-specific procedures and requirements. The plot highlights the balance maintained by each state in providing voter information, with clusters indicating common trends and outliers suggesting unique patterns of question specificity.
• Figure 4: US states leading and lagging in voter FAQ content quality, as assessed using cut-off of two standard deviation from mean on the metric (i.e., $\geq$ ($\mu$$\pm 2\sigma$); $\leq$ ($\mu$$\pm 2\sigma$)).
• Figure 5: Question Template Example