Jornalismo de dados e o redesenho algorítmico do processo de construção da notícia.

Abstract

Data and journalism have always been connected. The first helps the second to consolidate itself and deliver credible information to the public. With the advent of computers and of the Internet, the link between the two universes has narrowed. In the 1950s, when terms such as RAC (Computer Assisted Reporting) and Precision Journalism were used, it was difficult to access data. So the focus was on obtaining those elements that would give more credibility to the news and supply a weakness in the newsrooms. With technological developments, the focus is now on data processing and how it permeates the journalistic production process. Data Journalism represents a convergence of fields that are significant in their own right - Communication and Information Technology -, from investigative and statistical research, to design, programming and formatting of data, so that they are published. In this research, we seek to propose a way to optimize and incorporate the work with computational data in journalism newsrooms. The data are appropriate to define the composition and structure of the presentation of content of a journalistic nature and translate the facts from the numbers. We started from a bibliographic survey of journalism and data mining concepts for a proposal to remodel the news construction process. After this, with the perception of the model used in several communication companies around the world, we formatted a suggestion to form a Data Journalism team within the newsrooms. We realize that there are several steps to be followed, such as adequately training professionals who are interested in this area, investing in computer equipment and programs, as well as betting on a heterogeneous team. In the new flow designed for newsrooms, it is possible to see a hybrid work model, which crosses several areas of knowledge. We believe that this research can contribute to students and researchers who seek to unite Communication and Information Technology, as well as serve as a basis for software developers who need to understand the technological needs of a journalism company and, thus, solve existing problems. The work also reveals an academic importance for the training of journalists and computer scientists who want to improve themselves in a Data Science universe that is not focused on Journalism, but that establishes an equal dialogue between the two areas of knowledge.