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Summary Data Journalism all notes!
Notes from the lecture, seminar and readings.
[Meer zien]Voorbeeld 3 van de 19 pagina's
Voorbeeld 3 van de 19 pagina's
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Voorbeeld van de inhoud
Weekly questions1. What is data journalism?
Journalism: activity of gathering assessing, creating and presenting news and information
Data: points of information that are not relevant in a single instant but massively important when put
together and read from the right angle.
Data journalism differs from traditional journalism because it includes little points of information
put together in order to give meaning to daily occurrences while gathering filtering and visualising
events. It involves the understanding of events that we would not be able to without the use of
data, transforming something abstract in something that everyone can understand.
Data journalism is a broad genre that can include 3 distinct journalistic approaches:
- Computer assisted reporting (CAR)
- Data visualisation
- Algorithmic or automated journalism
“Data journalism is such a wide range now of styles - from visualisation to long form articles. The key thing
they have in common is that they are based on numbers and statistics - and that they should aim to get a
'story' from that data.”
2. How is data journalism developing across the world?
Depends on
- Accessibility of information
- Individual awareness
- government’s restrictions and data manipulation; political parties, religious organizations, military’s
and intelligence agencies’ pressures
- self-censorship to protect yourself as a survival strategy
- technical knowledge and second professional knowledge that help to align journalistic values and
ethics with the use of technology
In the west, data journalism is more developed than the east
3. What is the difference between data-driven reporting and traditional reporting?
Journalism seems to be taking a quantitative turn. As Coddingtn (2015) argued, as more information has
become ones and zeros (at its most elemental level) more journalism has included gathering, analysing and
computing that information as quantitative data as well.
4. How does data journalism changes between platforms?
One of the biggest impact is the ones that new professions have been created. Journalists learnt to work
with different technologies (eg: desiners).
The introduction of new digital technologies has transformed the roles descriptions, tasks and
responsibilities, ethical and economic interests, and the private professional temporalities of media
workers. The Financial crisis led to very high competition in the journalistic environment ruled by the
new attention economy.
Awareness of the behaviour of the users, from how they interact with the platform to their preferences in
the news type.
Liquid boundaries in Journalism: Liquid modernity demands flexibility and readiness to change, but at the
same time, it is a call to find ways to optimize the service journalists offer to society in the present digital
,era. Thus liquid journalism blurs the lines between the tasks of the journalists as they find themselves in
occupying different tasks.
Diversification of different roles in journalism: there are new profession in the selection of the agenda of
newsrooms, they are actually involved in the production of an article (different from earlier times where
they did not have much saying in the topic).
5. What impact does data journalism has over journalistic values?
On the one hand, doomsayers argue that interaction with new technological tools is responsible for the
decline of modern journalism and that might fear a breakdown in the value of which professional have.
Introducing technologies in the newsroom is causing high levels of distress between media workers
because they are constantly changes their practices
“On the other hand, journalists are actually just exhibiting an occupational identity that resembles the one
of the printed counterparts as defined partially by a traditional gatekeeping function and a disregard for
user author content (what??)”. Some core professional values remain the same as long as workers
identifies themselves as journalists keeping high the standards. Journalists are normalising the use of new
technologies.
6. Why some newsrooms have failed to implement this “quantitative turn in journalism?
- Monetary costs
- Temporality/Time requirements: in order to create a data journalistic project is a gamble: it might
happen that results are not significant and that time has been wasted.
- Education and preparation
7. Why data Journalism is called the new punk?
Punk stands for critical thinking, stand against the authorities, DIY ethos, anti-consumerism.
These characteristics apply to data journalism because due to the new available data existing on the web
everyone can do data journalism to tell a story that traditional media do not as far as they have skills.
Indeed working with data has difficulties.
Data is a game changer because it is available online.
Week 2
What skills, tools, and infrastructures are necessary to develop a successful data-based journalistic project?
Do media workers have all these skills and tools today? And if not, how are media organizations adapting
their workers to these technological changes?
Positivity vs positivism
Positivity (most data journalists have high hopes for the future of their particular subfield, convinced it is
on the rise) and positivism (data reporters are strong believers in the ability of method-guided research to
capture real and provable facts about the world) create what I would call an empirically self-assured
profession.
“Genealogy,” as defined by Foucault and who himself draws on the earlier work of Nietzsche, is a unique
approach to studying the evolution of institutions and concepts over time and one that might be
distinguished from history as such.
The “development” of a thing, a practice, or an organ has nothing to do with its progress towards a single
goal. A “genealogy of data journalism,” then, would uncover the ways that data journalism evolved in ways
, that its creators and practitioners never anticipated, or in ways that may have even been contrary to their
desires. It
The narrative of Apostles of Certainty grounds itself in three distinct U.S. periods which provide three
different perspectives on the development of data journalism.
- “Progressive Era” which was a period of liberal political ascendancy accompanied by the belief that
the both state and ordinary citizens, informed by the best statistics available, could make the world
a more just and humane place. Traditional news reporting largely rejected sociology’s emerging
focus on social structures and de-personalized contextual information
- 1950s and 1960s, when a few journalism reformers began to look to quantitative social science,
particularly political science and sociology, as a possible source of new ideas and methods for
making journalism more empirical and objective. They would be aided in this quest by a new set of
increasingly accessible databases and powerful computers.
- “computational” or “structured” journalism. In the current moment of big data and “deep
machine learning,” these journalists claim that journalistic objectivity depends less on external
referents but rather emerges from within the structure of the database itself. In this era journalism,
like social science, investigates if “big data” and non-causal forms of correlational behavioralism
could provide insights into social activity.
Journalism ought to rethink the means and mechanisms by which it conveys its own provisionality and
uncertainty. If done correctly, this could make journalism more like modern science, rather than less.
Lecture
Dutchman understands that news is the result of the product of news-workers.
News is a window to the world. The events that get covered in news set the frame of discussion between
citizens and the quality of civil debate depends on the quality of information available; however those
issues that do not get enough visibility are going to be forgotten by the public.
”There is, on the one hand, “ordinary” data journalism and, on the other hand, “thorough” data
journalism. They are characterized by contrasting traits: the former is manageable by one individual, can
be done on a daily basis, and can be included in the existing routines of news organizations if journalists
master specific tools. The latter is eminently collective and requires the mobilization of a range of skills
(journalism, computer science, statistics, graphic design), necessitate more time, primarily requires that
news organizations completely rethink their workflow, and is more a question of “mind-set” than a
question of mastering new tools.” - DeMaeyer et al. (2014).
In order to have thorough data journalism we need to rethink the work flow and infrastructures of
organisation.
Critics of this new trend have argued that this ‘MoJo’ phenomenon, which is indeed economically
convenient for the newsrooms, is causing a wave of “de-professionalization” between journalists. As
Blankenship put it, “in professional work settings, the more widespread, less ‘exclusive,’ and less
specialized knowledge is among members of a workforce, the less professional that workforce becomes”
(2016, p. 1057). Thus, the problem with having one worker carrying out tasks that not long ago used to be
done by different professionals, Blankenship suggested in his research, is that journalists have less
specialized expert knowledge.
Professionals use their phone to film and report news.
Mojo journalism thus refers to the fact by making a journalist using a phone they lose professional
media skills (microphones, tv cameras, lights etc..)
Data journalism is a broad genre that can include 3 distinct journalistic approaches:
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