CAREER: Computational Journalism: Integrating Algorithms and People in the Production of News Information
职业:计算新闻学:将算法和人整合到新闻信息的生产中
基本信息
- 批准号:1845460
- 负责人:
- 金额:$ 54.96万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-02-01 至 2025-01-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
该项目的目标是开发计算型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编织在一起,目的是降低成本,提高识别新新闻报道的效率、有效性和规模。通过一系列公开写作、专业讲习班和课程,旨在提高专业和有抱负的计算和数据记者的技能和能力的综合教育计划将加强这一目标。通过将计算和众包信息处理方法与传播学和新闻学中的新闻价值理论相结合,本研究将为计算新闻学领域产生基础知识和原则。该研究项目将(1)基于以用户为中心的对计算故事发现工具的需求评估来开发概念设计框架,(2)在一系列新的社会技术系统中实例化该框架,该新的社会技术系统将适应包括调查、事实核查和社会新闻在内的三种报道场景,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所起的作用提供见解。系统评价,包括在外地部署专业记者,将评估为了解新闻信息制作的效用和可持续性而开发的新工作流程和界面的效率和效力。Insights将进一步完善设计框架,并在开放源码情报和危机信息等相邻信息监控领域展示新的机会。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Computational News Discovery: Towards Design Considerations for Editorial Orientation Algorithms in Journalism
计算新闻发现:新闻业编辑定向算法的设计考虑
- DOI:10.1080/21670811.2020.1736946
- 发表时间:2020
- 期刊:
- 影响因子:5.4
- 作者:Diakopoulos, Nicholas
- 通讯作者:Diakopoulos, Nicholas
Journalistic Source Discovery: Supporting The Identification of News Sources in User Generated Content
- DOI:10.1145/3411764.3445266
- 发表时间:2021-05
- 期刊:
- 影响因子:0
- 作者:Yixue Wang;N. Diakopoulos
- 通讯作者:Yixue Wang;N. Diakopoulos
From Crowd Ratings to Predictive Models of Newsworthiness to Support Science Journalism
从人群评级到新闻价值预测模型以支持科学新闻
- DOI:10.1145/3555542
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Nishal, Sachita;Diakopoulos, Nicholas
- 通讯作者:Diakopoulos, Nicholas
Crowdsourcing Impacts: Exploring the Utility of Crowds for Anticipating Societal Impacts of Algorithmic Decision Making
众包的影响:探索群体在预测算法决策的社会影响方面的效用
- DOI:10.1145/3514094.3534145
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Barnett, Julia;Diakopoulos, Nicholas
- 通讯作者:Diakopoulos, Nicholas
Cataloging Algorithmic Decision Making in the U.S. Government
美国政府的算法决策编目
- DOI:
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Lee, Grace;Sinchai, Jasmine;Trielli, Daniel;Diakopoulos, Nicholas
- 通讯作者:Diakopoulos, Nicholas
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Nicholas Diakopoulos其他文献
Facebook’s News Feed Algorithm and the 2020 US Election
Facebook 的 News Feed 算法与 2020 年美国大选
- DOI:
10.1177/20563051231196898 - 发表时间:
2023 - 期刊:
- 影响因子:5.2
- 作者:
Jack Bandy;Nicholas Diakopoulos - 通讯作者:
Nicholas Diakopoulos
Defining Local News: A Computational Approach
定义本地新闻:计算方法
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Nick Hagar;Jack Bandy;Daniel Trielli;Nicholas Diakopoulos - 通讯作者:
Nicholas Diakopoulos
How Journalists Can Systematically Critique Algorithms
记者如何系统地批评算法
- DOI:
- 发表时间:
2020 - 期刊:
- 影响因子:0
- 作者:
Daniel Trielli;Nicholas Diakopoulos - 通讯作者:
Nicholas Diakopoulos
Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation
模拟政策影响:开发生成情景写作方法来评估监管的感知效果
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:0
- 作者:
Julia Barnett;Kimon Kieslich;Nicholas Diakopoulos - 通讯作者:
Nicholas Diakopoulos
Nicholas Diakopoulos的其他文献
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{{ truncateString('Nicholas Diakopoulos', 18)}}的其他基金
CHS: Small: Assessing the Role of Platform Algorithms in Shaping News Attention
CHS:小:评估平台算法在塑造新闻注意力方面的作用
- 批准号:
1717330 - 财政年份:2017
- 资助金额:
$ 54.96万 - 项目类别:
Standard Grant
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