VALENTINA S. BEREZHNAYA
National Research University Higher School of Economics,
Researcher ID: AAB-7697-2019
STANDARDIZED FACT CHECKING IN DATA JOURNALISM. A THEORETICAL VIEW
Abstract. Data journalism is based on data used both as a source of a story and as a proof for facts stated in journalistic investigations. Core principles of journalistic work are changing under the influence of data: working with data, acquiring datasets, verifying data, analyzing and presenting it in data stories is drastically different from traditional journalistic methods, while simultaneously continuing to be an organic part of journalistic research within the existing framework of journalism standards and ethical requirements. Fact-checking in data journalism is often limited to verifying correct math and analysis methods in data, whereas other factors defining the correctness and ethics of a journalistic product are ignored. Those include assessing the sources of data, methods and reasons of data collection, correctness of interpretation, contextual dependencies of data, correctness of visual representation of data analysis results, etc. Scientists are expanding research into the epistemological differences of data journalism from traditional journalistic practice, noting such distinct features as creating personal knowledge and its acceptance by the audience under the influence of data-driven practices and co-creation and crowd verification of data-based investigations. At the same time, academic research also focuses on data journalism fact-checking as a mere technological process of revision and comparison of calculations, not as a holistic system of data-story verification on multiple interconnected planes from technology to ethics. In this article, the author tries to fill the existing gap between academic research and actual data fact-checking practices in newsrooms by scrutinizing and evaluating various approaches to data-story fact-checking in a number of media, and consequently defining white spaces in the data factchecking workflows. Lack of professional standards in the area allows for lower quality of publications, as well as publishing wrongly interpreted or presented data, whether by mistake or by intent. This prompted the author’s original view of fact-checking in data journalism as a system of consistent multilevel assessment.
Keywords: data journalism, fact checking, reproducibility, interpreting and contextualizing data, data analysis, standards