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News bot for the newsroom: how building data quality indicators can support journalistic projects relying on real-time open data

par Dierickx, Laurence
Référence Global Investigative Journalism Conference 2017(16-19/11/2017: Johannesburg), Global Investigative Journalism Conference 2017 Academic Track, Investigative Journalism Education Consortium
Publication Publié, 2018-02-02

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Résumé :

Storytelling with data has become a trend in journalism with the rise and growth of data-driven journalism practices encompassed in the field of computational journalism. It aims to provide quality information, which may not be made possible without high quality data. According to the ISO 9000, definition of quality is regarded as the ability to satisfy stated or implicit needs.This paper proposes a conceptual framework to assess data quality with a combination of deterministic and empirical quality indicators. If data quality is a multidimensional concept, the object is here to establish how to fit the needs of journalistic projects. Formal quality indicators are essentials when data are collected and/or automated. We can call it the technical challenge. Empirical indicators are also essentials regarding professional practices. We can call it the journalistic challenge. This part of the paper also demonstrates how and why data quality literacy is able to meet and to support journalistic requirements.This framework was applied in the context of a case study. “Bxl’air bot” is an automated web application (news bot) fed with real-time open data about air quality in Brussels. Air quality can be here considered as a social problem, which concerns environment, mobility, economic and health politics. The news bot aims to objectivate a situation regarding the European’s norms as well as the World Health Organizations’ recommendations. The news bot is here considered as an object of journalism and as a tool for the Belgian newsroom Alter Echos involved in this research project.If the framework has helped to make appropriate conceptual decisions, from a constructivist approach, the necessity to well understand the data lifecycle was also highlighted to complete the framework.