Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism
Table Scraps: An Actionable Framework for Multi-Table Data Wrangling From An Artifact Study of Computational Journalism
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表碎片：来自计算新闻学工件研究的多表数据争论的可行框架
DOI：
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发表时间：
2020
影响因子：
5.2
通讯作者：
T. Munzner
中科院分区：
文献类型：
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作者：
Stephen Kasica;C. Berret;T. Munzner
对于许多使用数据和计算来报道新闻的记者来说，数据争论是他们工作中不可或缺的一部分。尽管在企业数据分析的背景下有大量关于数据争论的文献，但很少有人知道记者在执行这项乏味、耗时的任务时遇到的具体操作、流程和痛点。为了更好地了解这一用户群体的需求，我们对26家新闻机构的33名专业记者撰写的50个公共数据存储库和分析代码进行了技术观察研究。我们开发了计算新闻中数据争论的两个详细和交叉的分类法，用于行动和过程。我们观察到多表的广泛使用，这是以前争论分析中的一个显着差距。我们为一般的多表数据争用开发了一个简洁、可操作的框架，其中包括分类法中记录的争用操作，这些操作在其他工作中没有明显的相似之处。这个框架是第一个将表合并为一级对象的框架，它将支持未来用于计算新闻和通用用途的交互式争论工具。我们通过讨论其与我们的分类法集的关系来评估我们框架的生成和描述能力。
For the many journalists who use data and computation to report the news, data wrangling is an integral part of their work. Despite an abundance of literature on data wrangling in the context of enterprise data analysis, little is known about the specific operations, processes, and pain points journalists encounter while performing this tedious, time-consuming task. To better understand the needs of this user group, we conduct a technical observation study of 50 public repositories of data and analysis code authored by 33 professional journalists at 26 news organizations. We develop two detailed and cross-cutting taxonomies of data wrangling in computational journalism, for actions and for processes. We observe the extensive use of multiple tables, a notable gap in previous wrangling analyses. We develop a concise, actionable framework for general multi-table data wrangling that includes wrangling operations documented in our taxonomy that are without clear parallels in other work. This framework, the first to incorporate tables as first-class objects, will support future interactive wrangling tools for both computational journalism and general-purpose use. We assess the generative and descriptive power of our framework through discussion of its relationship to our set of taxonomies.