用概率模型挖掘地方政府会议中公众声音，提升基层民意捕捉效率。
PUBLICSPEAK: Hearing the Public with a Probabilistic Framework in Local Government
- 基于会议结构与语言特征的联合概率模型，自动识别公众发言
- 在7个美国城市数据上，准确率比现有方法平均提升10%，最高达40%
- 适合关注基层治理、政策反馈分析的研究者与公共部门
全球各地地方政府需代表居民做出重要决策，而居民则通过公开会议提出诉求、建议和对官员的评价。然而，大量小型会议无法被传统新闻机构规模化覆盖。本文提出PUBLICSPEAK，一种利用会议结构、领域知识和语言信息的概率框架，用于发现地方政府会议中的公众言论。我们将其应用于美国7个城市的会议数据，评估结果显示，该方法在新构建的本地政府会议数据集上，相比现有最优技术平均提升10%，最高达40%。
原文摘要 · Abstract (English)
Local governments around the world are making consequential decisions on behalf of their constituents, and these constituents are responding with requests, advice, and assessments of their officials at public meetings. So many small meetings cannot be covered by traditional newsrooms at scale. We propose PUBLICSPEAK, a probabilistic framework which can utilize meeting structure, domain knowledge, and linguistic information to discover public remarks in local government meetings. We then use our approach to inspect the issues raised by constituents in 7 cities across the United States. We evaluate our approach on a novel dataset of local government meetings and find that PUBLICSPEAK improves over state-of-the-art by 10% on average, and by up to 40%.
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