@inproceedings{rawte-etal-2025-voters,
    title = "Do Voters Get the Information They Want? Understanding Authentic Voter {FAQ}s in the {US} and How to Improve for Informed Electoral Participation",
    author = "Rawte, Vipula  and
      Scott, Deja N  and
      Kumar, Gaurav  and
      Juneja, Aishneet  and
      Yaddanapalli, Bharat Sowrya  and
      Srivastava, Biplav",
    editor = "Cao, Trista  and
      Das, Anubrata  and
      Kumarage, Tharindu  and
      Wan, Yixin  and
      Krishna, Satyapriya  and
      Mehrabi, Ninareh  and
      Dhamala, Jwala  and
      Ramakrishna, Anil  and
      Galystan, Aram  and
      Kumar, Anoop  and
      Gupta, Rahul  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.trustnlp-main.14/",
    pages = "185--231",
    ISBN = "979-8-89176-233-6",
    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 \textbf{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 \url{https://anonymous.4open.science/r/election-qa-analysis-BE4E}."
}