CRII: III: Measuring the alignment between the interests of local communities, local news, and the national news media

由于公民的兴趣将国家和地方社区话题结合起来，他们与网络新闻来源的互动有望反映这种关注的二元性。例如，在线新闻应该将有关当地学区的报道和有关国家利益的报道结合起来。在这个奖项中，研究人员将促进我们对地方和国家新闻报道如何满足当地社区的需求和利益的理解。研究结果可以为更有效的沟通策略提供信息，并提高我们对影响社区的关键问题的认识。这项研究包括四个主要重点。首先，创建一个新的纵向本地新闻存储库，以补充研究人员在过去与互联网档案馆合作中率先开发的现有纵向国家新闻存储库。其次，该项目将研究新的自然语言处理（NLP）方法，以识别新闻覆盖或不覆盖的地理位置，在快速变化的新闻格局中提供新闻沙漠的动态测量。第三，该项目将挖掘社会媒体话题和搜索趋势所表达的社区兴趣和需求。第四，该项目将探索当地社区利益与新闻主题之间的一致性。通过实现这些目标，本研究将为建立纵向本地新闻数据集创造新的技术和最佳实践，这可能是来自不同学科的研究人员通过本地媒体研究美国的宝贵资源。此外，这项工作将开创新的NLP模型，用于识别新闻报道所覆盖的社区，并绘制当地新闻沙漠的范围，以揭示被忽视和边缘化的社区。此外，拟议的活动将量化社区利益与新闻故事之间的一致程度。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

As citizen interests combine national and local community topics, their interaction with online news sources is expected to reflect this duality of focus. For example, online news should combine stories about local school districts and ones about national interests. In this award, the researchers will advance our understanding of how local and national news reports address the needs and interests of local communities. The findings could inform more effective communication strategies and improve our knowledge of critical issues affecting communities.This research comprises four main thrusts. First, creating a novel longitudinal local news repository to complement an existing longitudinal national news repository the researchers pioneered in a past collaboration with the Internet Archive. Second, the project will research new natural language processing (NLP) approaches to identify the geographic locations covered or not covered by the news, to provide a dynamic measurement of news deserts in a rapidly changing news landscape. Third, the project will mine community interests and needs expressed by topics on social media and search trends. Fourth, the project will explore the alignment between the interests of local communities and news topics. By fulfilling these goals, this research would create new techniques and best practices in building longitudinal local news datasets, which could be a valuable resource for researchers from different disciplines to study the United Stated via local media. Additionally, this effort would pioneer novel NLP models for identifying communities covered by news reports, and map the extent of local news deserts to unveil neglected and marginalized communities. Moreover, the proposed activities will quantify the degree of alignment between community interests and news stories.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.