Understanding Data Journalism
What, exactly, does data journalism mean in an age of open data portals, dazzling visualizations and freedom of information battles around the world? A dictionary definition of the two words doesn’t help much—put together, it suggests that data journalism is an occupation of producing news made up of facts or information. Data journalism has come to mean virtually any act of journalism that touches electronically held records and statistics—in other words, virtually all of journalism.
That’s why a lot of people in the field don’t think of themselves as data journalists— they’re more likely to consider themselves explanatory writers, graphic or visual journalists, audience analysts, or news application developers—all more precise names for the many tribes of this growing field. That’s not enough, so add in anything in a newsroom that requires the use of numbers, or anything that requires computer programming. What was once a garage band has now grown big enough to make up an orchestra.
Taxonomies of different branches of data journalism can help students and practitioners clarify their career preferences and the skills needed to make them successful. These different ways of doing data journalism are presented here in an approximate chronology of the development of the field.
Empirical journalism, or data in service of stories
Maurice Tamman of Reuters coined the term “empirical journalism” as a way to combine two data journalism traditions. Precision journalism, developed in the 1960s by Philip Meyer, sought to use social science methods in stories. His work ranged from conducting a survey of rioters in Detroit to directing the data collection and analysis of an investigation into racial bias in Philadelphia courts. He laid the groundwork for investigations for a generation. Empirical journalism can also encompass what became known as computer-assisted reporting in the 1990s, a genre led by Eliot Jaspin in Providence, Rhode Island. In this branch, reporters seek out documentary evidence in electronic form—or create it when they must—to investigate a tip or a story idea.
More recently, these reporters have begun using artificial intelligence and machine learning to assist in finding or simplifying story development. They can be used to help answer simple questions, or to identify difficult patterns.
These reporters are almost pure newsgatherers—their goal is not to produce a visualization nor to tell stories with data. Instead, they use records to explore a potential story. Their work is integral to the reporting project, often driving the development of an investigation. They are usually less involved in the presentation aspects of a story.(https://www.daowen.com)
Arguably the newest entry into this world of “data journalism” could be the growing impact of visual and open-source investigations worldwide. This genre, which derives from intelligence and human rights research, expands our notion of “data” into videos, crowdsourced social media and other digital artefacts. While it’s less dependent on coding, it fits solidly in the tradition of data journalism by uncovering—through original research—what others would like to hold secret.
Data visualization
Looking at the winners of the international Data Journalism Awards would lead a reader to think that visualization is the key to any data journalism. If statistics are currency, visualization is the price of admission to the club. Visualizations can be an important part of a data journalist’s toolbox. But they require a toolkit that comes from the design and art world as much as the data, statistics and reporting worlds. Alberto Cairo, one of the most famous visual journalists working in academia today, came from the infographics world of magazines and newspapers. His work focuses on telling stories through visualization—a storytelling role as much as a newsgathering one.
Words & Expressions
portal：大门；门户网站
orchestra：管弦乐队
taxonomy：分类学
practitioner：从业人员；专门人才
chronology：年表
empirical：以实验为依据的