NewsCollab: Fostering Data-driven Journalism with Crowdsourcing
Ygor Rolim, Danielly Alves, Flávia Clemente, Daniel de Oliveira · 2023
The amount of data produced over the last decade has increased at a fast pace. Many domains of knowledge were completely transformed by the availability of massive datasets. Journalism is one of these domains. Commonly, news articles are produced by analyzing massive datasets containing raw data and previously published articles. This new type of journalism is called data-driven journalism. However, data-driven journalism requires that previously published articles are accessible, categorized, and organized. This can be a laborious task to be manually performed. Starting from the assumption that news articles should be able to be imported from multiple sources, and collaboratively analyzed and categorized by other journalists (to avoid bias), we propose the use of a new collaborative system named NewsCollab. NewsCollab allows a multitude of journalists to download news articles from different portals and provide feedback on these articles, giving to the journalistic community a better understanding of the published articles that can be used to produce new material.