CAREER: Computational Journalism: Integrating Algorithms and People in the Production of News Information

该项目的目标是开发计算新闻报道发现工作流程和工具，将专家记者、在线人群贡献者和算法结合在一起，旨在降低成本，提高效率、有效性和规模，从而可以识别新的新闻报道。这一目标将通过一项综合教育计划得到加强，该计划旨在通过一系列公开写作、专业讲习班和课程，提高专业和有抱负的计算和数据记者的技能和能力。通过将计算和众包信息处理方法与传播学和新闻学的新闻价值理论相结合，本研究将为计算新闻领域提供基础知识和原则。在特定领域之外，贡献将推动和催化信息科学和人机交互的更广泛领域的研究，这些领域涉及信息生产混合过程的效率和有效性，以及信息接口的设计，使专家能够更有效地监测世界上的重要事件。该研究项目将(1)开发一个基于以用户为中心的计算故事发现工具需求评估的概念设计框架，(2)在一系列新的社会技术系统中实例化该框架，这些系统将适应三种报道场景，包括调查、事实核查和社会新闻，以及(3)产生关于故事发现工具的成本效率和有效性的经验知识。这将为算法在支持公共利益新闻信息的可持续性方面的作用提供见解。系统评价，包括专业记者的实地部署，将评价所制订的新工作流程和界面的效率和效力，以便了解对新闻资料制作的效用和可持续性的潜在影响。insight将进一步完善设计框架，并在相邻的信息监控领域（如开源情报和危机信息学）中揭示新的机会。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。

The goal of this project is to develop computational news-report discovery workflows and tools that weave together expert journalists, online crowd contributors, and algorithms, with the intent of lowering the cost and increasing the efficiency, effectiveness, and scale at which new news reports can be identified. This goal will be reinforced by an integrated education plan aimed at advancing the skills and capabilities of professional and aspiring computational and data journalists through a series of public writings, professional workshops, and curricula. By synthesizing computational and crowdsourced information processing approaches with theories of newsworthiness from communication and journalism studies, this research will produce foundational knowledge and principles for the field of computational journalism. Beyond the specific domain, contributions will advance and catalyze research in broader fields of information science and human-computer interaction in relation to the efficiency and effectiveness of hybrid processes for information production, and the design of information interfaces to enable experts to more effectively monitor the world for important events.The research project will (1) develop a conceptual design framework based on a user-centered needs assessment of computational story discovery tools, (2) instantiate that framework in a series of novel sociotechnical systems that will be adapted to three reporting scenarios including investigative, factchecking, and social journalism, and (3) produce empirical knowledge about the cost efficiency and effectiveness of story discovery tools, which will provide insights into the role of algorithms in supporting the sustainability of public interest news information. System evaluations, including field deployments with professional journalists, will assess the efficiency and effectiveness of the novel workflows and interfaces developed in order to understand potential impacts on the utility and sustainability of news information production. Insights will further refine the design framework and expose new opportunities in adjacent information monitoring domains such as open source intelligence and crisis informatics.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.