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

arXiv: https://arxiv.org/abs/2412.15273 · HTML: https://mlsanity.com/paper/2412.15273/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Datasets: A Community Library for Natural Language Processing](https://mlsanity.com/paper/2109.02846/) (2021)
- [Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements](https://mlsanity.com/paper/2001.00623/) (2020)
- [FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering](https://mlsanity.com/paper/2306.05523/) (2023)
- [Bots, Disinformation, and the First Trump Impeachment](https://mlsanity.com/paper/2204.08915/) (2022)
- [On Safe and Usable Chatbots for Promoting Voter Participation](https://mlsanity.com/paper/2212.11219/) (2022)

## Abstract (arXiv)

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 FIQStextsubscriptvoter, while 14% were leaders and 12% laggards for FIQStextsubscriptdeveloper.