Automated Journalism in Data Visualization

2,508 views

Published on

Can robot journalism help automatically annotate and describe what a viewer is seeing on data visualizations?

1 Comment
4 Likes
Statistics
Notes
No Downloads
Views
Total views
2,508
On SlideShare
0
From Embeds
0
Number of Embeds
751
Actions
Shares
0
Downloads
27
Comments
1
Likes
4
Embeds 0
No embeds

No notes for slide

Automated Journalism in Data Visualization

  1. 1. Automated Journalism in Data Visualisation
  2. 2. There has been an increased interest, focus, and analysis of storytelling this year in the field of data visualisation “This has unquestionably been the year of ‘storytelling’” - Visualisingdata.com “Storytelling with data visualization is still very much in its “Wild West” phase” - Nick Diakopoulos “Storytelling: The Next Step for Visualization” - Robert Kosara “Storytelling has been one of the big buzzwords in data visualization the last year” - Moritz Stefaner “There is more to information visualization than punchlines.” - Moritz Stefaner ....But there are also some people questioning it
  3. 3. With many great examples of stories being told using “interactive” techniques Snow Fall by the New York Times is a stand out.
  4. 4. Some of these broadly include “visualisations” with varying levels of “interactiveness”
  5. 5. Interactive story telling is nothing new. Interactive fiction, role playing games, etc. have all existed for many many years. Indeed, during the 1970 and 80s researchers explored the topic. Chris Crawford’s book on the topic is almost a decade old.
  6. 6. Quokka from the late 1990s was well ahead of its time and arguably was a better, more scalable example of rich, interactive story telling, in real time, than one off pieces taking 7 months to build within a major news organisation. As an aside Quokka’s creative director was Eric Rodenbeck who when on to found Stamen Design.
  7. 7. This presentation is not about story telling, but rather the scalability of story telling and more specifically annotated journalism in the field of data visualisation.
  8. 8. Today, most visualisations with a journalism or story telling are developed with a static, somewhat historical, set of data.
  9. 9. Within these data visualisations, the opportunity exists for careful analysis and considered commentary to be created that augments the visualisations, annotating salient points, and assisting the viewer in interpreting and understanding what they are looking at. The annotation layer of graphics is “the most important thing we do.” - Amanda Cox, NYT graphics editor
  10. 10. But what happens when data visualisations are built that are going to be updated with data for the next 1, 2 or 6 years, when the capacity for expert analysis every month across hundreds or thousands of schools, hospitals, train stations etc. is not possible.
  11. 11. Could robotic journalism be used to auto-annotate data visualisations through their lifespan.
  12. 12. Automated journalism, robot journalism, narrative science are all terms that describe “automatically” generating online articles about particular topics. Taking statistical and raw data, the basic building blocks of most visualisations, smart “expert in a box” algorithms interpret and “analyse” the data, extracting salient points that are then fed into a language generator that outputs a brief, human-like article. Like spam comments only mathy.
  13. 13. In some ways it is the natural extension of the googl-ised, twitter-ised world that exists today in which “trending” stories are given equal or more weight on news sites than deep journalism. The ultimate in snackable content.
  14. 14. Companies exist today that use artificial intelligence style algorithms to transform data into stories and insights. StatSheet takes sports scorecards, analyses them looking for patterns, isolates key points and produces an article highlighting these insights. When you think about it, this seems entirely plausible given the robotic, repetitive and repeatable nature of sports commentators. Narrative Science does a similar thing but across the more “serious” topics of financial services and information research.
  15. 15. The size of the problem domain is already huge and only growing, especially as demand and expectations for data visualisations to outlive the year grow. “It seems to me that annotations, as an integral part of a visualization design, have received somewhat little attention in comparison to other components of a visual representation” - Enrico Bertini
  16. 16. We’ve all heard Borel’s imagery of monkeys in a room typing Shakespeare, but in this new world of Big Data, is it possible that automated journalism could be used to annotate and enhance data visualisation.
  17. 17. To date, few examples exist of successful implementations of this style of annotation in data visualisation, especially beyond the pre-school level “You have done 45% better this year”, “Your performance is good”. But the gap is closing. There have been some initial efforts at automatic “labeling” but much less on humanised text.
  18. 18. Schooloscope by Berg is a great example of beautiful aesthetics and design along with an humanised textual annotation that summarises what the visualisation is showing.
  19. 19. So, what do you think? Do you have examples of automated annotation in data visualisation? Is it the future? If so, utopian or dystopian? We welcome your thoughts. ben@flinklabs.com @flinklabs This is a slide pack was prepared to help develop thoughts around automated storytelling and journalism in data visualisation. The thoughts and points are not fixed in stone.

×