Do Voters Get the Information They Want? Understanding Authentic Voter FAQ s in the US and How to Improve for Informed Electoral Participation (2025)

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 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 FIQSvoter, while 14% were leaders and 12% laggards for FIQSdeveloper. The code and sample data are provided at https://anonymous.4open.science/r/election-qa-analysis-BE4E .

## introduction democracy is the leading form of governance where people have a say in who governs them. its success depends on the ability of participants to vote in regular elections and the ability of the government to implement the subsequent orderly transfer of power @xcite . democracy at a practical level means empowering the voter with a right to choose and providing all relevant and reliable information including knowing about candidates, campaign finance, voting procedure, processing of votes, and declaration of results. however, around the world, stakeholders are struggling to find accurate information, which is now especially acute in the age of generative artificial intelligence (ai) and other technologies from the natural language processing (nlp) and wider ai communities. the situation is so bad with information gaps and disorders that whenever ai is referenced in connection with elections, it often draws negative reactions due to the fear of bots, misinformation, and hacking. as a baseline and illustration of the current situation, for elections, openai declared that chatgpt will defer election questions to humancurated frequently asked questions (faqs) (ope-nai, 2024), even though it has one of the best performance in qa settings. this is particularly disappointing for ai, and especially chatbots, or bots, for short, since they are multi-modal collaborative assistants that have been studied since the early days of ai to help people complete useful tasks. for elections, people could have overcome voting complexity by accessing authentic information conveniently in their own language or words through their smartphones, computers, and home devices. in the united states (us), state election commissions (secs), often required by law, are the primary providers of frequently asked questions (faqs) (see fig. 1 ) to voters, and secondary sources like non-profits such as league of women voters (lwv) try to complement their information shortfall. however, there is a general perception that it is hard to find the right, accurate information and in its absence, the democratic processes are under increasing threats like information disorders, a term which covers misinformation, disinformation and misinformation @xcite @xcite @xcite . however, surprisingly, to the best of our knowledge, there is neither a single source with comprehensive faqs nor a study analyzing the data at a national level to identify current practices and ways to improve the status quo. in response, we provide a dataset on voter faqs for the nlp community covering all the us states. we next present the related work, followed by data and nlp methods, and then analyze the faq data. we use the analysis to identify guidelines that lagging and mainstream states can adopt and conclude. in summary, our key contributions are: ## related work going beyond studying the negative impacts of ai on elections, researchers have begun shifting attention to its positive impacts. ai-driven tools can enhance voter education by offering personalized, real-time responses to common election questions, and they can support policymakers by identifying trends and disparities in voter access or participation. by providing actionable insights, ai could play a transformative role in improving the transparency and efficiency of electoral systems. in this regard, (srivastava, 2025) outlines their cdc approach of (i) collating frequently anticipated questions and their authoritative answers, (ii) distributing reliably by modeling multidimensional opinion networks with official information and discovering strategies to control them @xcite , and (iii) helping people with diverse backgrounds comprehend official information with personalization and provenance using chatbots @xcite ,all in the service of reducing information gap for increasing voter participation. but it all starts with authentic data. in us, questions about state-specific election processes -such as voter registration, polling locations, absentee ballot rules, and early voting policies -are crucial for both voters and policymakers. however, the decentralized nature of u.s. elections means that this information is often fragmented across various state and local jurisdictions, creating barriers to accessibility and analysis. ai has the potential to address these challenges by aggregating, standardizing, and analyzing election-related data. releasing datasets is a key tradition in advancing nlp research, often catalyzing further work in the field. related datasets, such as factify3m @xcite , have enhanced online information reliability. our dataset adheres to the nlp community's best practices. niques, analysis setup, and the novel composite metrics introduced for comparing us states. ## guidelines for improving the ecosystem we note that figure 2 gave a composite view of the leading and lagging us states in content quality, of which an illustration was shown in figure 1 . digging deeper, we found that leaders do a few things differently (and correctly), which others should follow. they are that leaders have more questions and answers ( based on these analyses, we provide the following guidelines for all states to improve their voter faq content. they are that states should (1) provide a reasonably large number of questions (@xmath0, (2) provide precise and specific answers that are not too terse, (3) reduce overlap across questions by reducing overlap of topics, and (4) keep sentiment of content neutral. ## conclusion and future work this paper makes many contributions, starting by addressing the challenges faced by voters in finding answers to their election-related questions by providing the first dataset on voter faqs covering all the us states. second, we introduce metrics for faq information quality score (fiqs) 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. we provide verified, curated voter information to counteract widespread misinformation. this work, although promising, is just the first step. in future, one can work to remove the limitations and also build decision-support tools using the data to make effective tools available to voters. one can also separate the analysis by secs, the primary, official data providers, and by secondary sources, e.g., non-profits like lwv, to understand how they complement each other.