| dc.description.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. | eng |
