E45 - Computational Journalism: The Data Behind the Stories with Jonatha...

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E45 - Computational Journalism: The Data Behind the Stories with Jonatha...
Fashioning the Future
Video 1: Introduction to Fashioning the Future by Ashlee Murphy
The fashion journalist, once the gatekeepers to the fashion capitals of the world and their runways, are now fleeting to rule the World Wide Web.This expansion comes from not just the breadth of opportunities and advancements known to social media platforms such as Instagram, Twitter and yes, even Tumblr, but also to keep up with the relentless battle against the fashion blogger and the decline of print media. Starting the conversation for this structural shift to modern journalism is important to divulging the true impact of technology and social media - is it a friend or foe to the future of journalism?
The question of what can and cannot be calculated, however, is not just a purely technical question; it is also a deeply social, cultural, political and economic one. For the participants in this study, the computational is never just about an algorithm, or a content management system but also about what these systems do in relation to existing beliefs, values, practices and culture.
Bucher, T. (2016). Machines dont have instincts: Articulating the computational in journalism
Overview of <a href=‘http://www.nytimes.com/interactive/2015/05/28/upshot/you-draw-it-how-family-income-affects-childrens-college-chances.html?_r=0’>“You Draw It: How Family Income Predicts Children’s College Chances”</a>
Katerina Iliakopoulou
This New York Times interactive piece focuses on how family’s income could potentially influence a child’s chances of higher education. Although it is primarily a data story, what makes the piece unique is the interactive graphs that aim to help readers understand journalists’ findings in an intuitive way.
The main feature that catches readers’ attention, is the use of imperative voice in the header: “You draw it”. Why would the authors ask me to do anything other than reading the article? There lies what distinguishes the story from other data pieces in a very clever manner. If we visualize the data on the family income and % of kids going to college, the result is a straight line showing that the relation between the two is extremely linear. In other words, the higher the income the higher a child’s chances of going to college.
This is a remarkable finding, but unfortunately the data visualization is quite dull. After all, it’s just a straight line and with so many other cool data visualizations out there, this graph could easily go unnoticed. To escape that trap, the editors took another approach: They decided to let the readers predict the relation in the data and essentially interact with the story and its findings. After the reader draws a potential prediction, she thinks is right, the graph shows how far she was in her estimates. She might be very close to the correct plot at some points, or rather off at other parts. Thus, the piece drives the reader to question what the story is trying to tell her and comprehend it through a self-taught process.
The interactive graph even has an eye for those lazy ones who just browse the Upshot for cool interactive graphs, without reading what the story is about:
Finally, the piece constantly improves itself by aggregating readers’ input and comparing current reader’s estimate to previous ones. Consequently, the article not only uses data to showcase a noteworthy phenomenon, but also chooses a creative form of storytelling to engage the reader.
The data the story is based upon was acquired from a research a team of economists did, regarding children born in the early 1980s. The journalists provide a link to the scientific publication that showcases the results of the research, which was released in 2014. In the publication there are detailed tables describing the different relations between family’s income and children’s college attendance, which if processed and aggregated produce the final graph presented in the data story. The data journalists analyze research results with relation to the graph they created, and provide additional context with links to other researches than the one they used, that study how family’s income influence children’s chance in higher education.
One thing that is missing from the story is real human experience that could nicely frame the information the article is trying to communicate. For example, an interview with a high school student about her prospects of attending college based on her family’s financial state. Right now the piece is showcasing a remarkable pattern in society through data, but including interviews with real people would undoubtedly put a human face on it.
At a time when “big data” is in vogue and computational journalism is taking off, reporters need efficient ways to process millions of documents. TheDeclassification Engine is one way to solve this problem. The project uses the latest methods in computer science to demystify declassified texts and increase transparency in government documents.
The project’s mission is to “create a critical mass of declassified documents by aggregating all the archives that are now just scattered online,” said Matthew Connelly, professor of international and global history at Columbia University and one of the professors directing the project, in a phone interview with Poynter.
Check out my article about how The Declassification Engine proposes to use Natural Language Processing and machine learning algorithms to uncover redacted text in declassified documents.
Could it change the way we cover stories about the government?
I began writing this essay because I wanted to say something very simple: all of these things — journalism, search engines, Wikipedia, social media and the lot — have to work together to common ends. There is today no one profession which encompasses the entirety of the public sphere. Journalism used to be the primary bearer of these responsibilities — or perhaps that was a well-meaning illusion sprung from near monopolies on mass information distribution channels. Either way, that era is now approaching two decades gone. Now what we have is an ecosystem, and in true networked fashion there may not ever again be a central authority.
"What Should the Digital Public Sphere Do?" Jonathan Stray.
Automated journalism: Computers are already reporting the news