Collaborative research: FW-HTF-R: The future of news work: Human-technology collaboration for journalistic research and narrative discovery

这个项目探索新闻工作和计算工具的未来共同发展。研究记者和新闻工作很重要，因为即使面临许多挑战，新闻机构仍然是一股强大的力量，通过提供有关事件和趋势的信息，帮助人们对自己的生活做出更好的决定，保持社区安全和健康。新闻业作为一个以信息为基础的行业和职业，一直受到新技术的影响，最近的影响是互联网的兴起和广告收入的减少，以及新闻价值观与商业利益之间长期存在的紧张关系。这些压力对新闻流程和产品造成了损害。有些重要的故事没有被讲出来：它们需要花费太多时间去发现、研究和制作叙事，而时间越来越短缺。该项目旨在通过实地调查和以用户为中心的互动工具开发相结合来解决这些问题，以支持记者进行研究和叙事发现，即发现数据所讲述的故事，并以有趣和相关的方式将该故事传达给读者。该项目将通过实地研究和以用户为中心的交互式工具开发，促进对技术和新闻工作的了解，以支持记者进行研究和叙事发现。由此产生的技术支持的工作流程将通过自由记者的实验和在新闻编辑室的部署进行评估。拟议的项目由几个相互交织的组成部分组成，每个部分都有一个特定的重点，但旨在相互支持并导致研究趋同：(1)专注于更好地理解工人，他们的工作，特别是他们目前与新闻编辑室技术的关系；(2)构建和评估支持新闻研究的方法，特别是在不同类型的大型文档集合中查找相关信息，使用深度学习技术进行实体提取和共参考解析（AllenNLP）、语义搜索（BERT）和常识推理（COMET）；(3)建立和评估支持叙事发现的方法。产生的工具将在实地试验中加以评价，并通过它们对实地研究审查的工作的影响加以评价。这两套工具稍后将通过添加处理音频数据的能力得到增强。评估将扩展到考虑在人类记者和计算工具之间分配任务的替代方法。贯穿整个项目的将是评价、综合、传播调查结果和工具、规划和项目管理方面的工作。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。

This project explores the future co-evolution of news work and computational tools. Journalists and news work are important to study because, even in the face of numerous challenges, news organizations remain a powerful force for keeping communities safe and healthy by providing information about events and trends to help people make better decisions about their lives. Journalism, as an information-based industry and profession, has always been impacted by new technologies, most recently by the rise of the Internet and loss of advertising revenue, as well as the long-standing tension between journalistic values and business interests. These pressures take a toll on journalistic processes and products. Some important stories are not getting told: they take too much time to discover, research, and craft into narratives, and time is increasingly in short supply. The project seeks to address these problems through a combination of fieldwork and user-centered development of interactive tools for supporting journalists in research and narrative discovery, i.e., finding the story that the data tell and interesting and relevant ways to convey that story to a reader. This project will advance knowledge about technology and news work through field studies and user-centered development of interactive tools for supporting journalists in research and narrative discovery. The resulting technology-supported work processes will be evaluated in experiments with freelance journalists and through deployment in newsrooms. The proposed project is composed of several interwoven components, each with a specific focus, yet designed to be mutually supportive and to lead to research convergence: (1) focus on better understanding workers, their work and especially their current relationship to technology in newsrooms; (2) build and assess ways to support journalistic research, specifically finding relevant information in large document collections of different kinds, using deep learning techniques for entity extraction and coreference resolution (AllenNLP), semantic search (BERT) and common sense reasoning (COMET); and (3) build and assess ways to support narrative discovery. Resulting tools will be evaluated in field experiments and through their impacts on work examined in field studies. Both sets of tools will later be augmented by adding the ability to work on audio data. Assessment will be extended to consider alternative ways to divide tasks between human journalists and the computational tools. Running throughout the project will be work on evaluation, synthesis, dissemination of findings and tools, planning and project management.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.