Talk:Data journalism

Journalism Mid‑importance

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Requested move

Datajournalism → Data journalism – Existing references and internet searches indicate it should be two words 68.165.77.185 (talk) 01:50, 25 December 2012 (UTC)[reply]

Merge with computer-assisted reporting

There is a lot of conceptual overlap between "computer-assisted reporting" and "data journalism" to the point that I'm not aware of any significant difference beyond the use of data journalism as an informal descriptor for the same field. Without some compelling reason, I think its best to merge these two articles. Actongorton (talk) 18:27, 9 February 2016 (UTC)[reply]

Data journalism, computer-assisted reporting and computational journalism are not mutually exclusive. Whilst they have similar professional and epistemological roots, they will inevitably overlap. They are quite different terms and deserve separate articles. This research report^[1] explains and includes an important infographic that illustrates this nicely. Keep these as separate articles. If somebody has the time, it would be good to update this article with some notes from this paper. Rgesthuizen (talk) 07:21, 17 April 2017 (UTC)[reply]

"Computer-assisted reporting" and "data journalism" have no necessary overlap at all. As can be seen from historical examples. Today of course computers are used, but their use is pervasive in nearly all disciplines.

That said, I suggest adding a section titled "Criticism." This could be helpful to practitioners. Examples:

1) Nonsensically implied causal relationships. Many or most data journalism articles explicitly suggest or imply a causal relationship between variables. But data journalists seem happily ignorant that their inferences are nearly always nonsense. Indeed, it is rare to find a data journalism article that does not involve one of these fallacies: https://wiki.riteme.site/wiki/Questionable_cause. And more. Absurdly biased samples, ecological fallacies, and so on. Of course causal models generally oversimplify reality, but oversimplifying with a dopey scatterplot or line chart (See!, B followed A!) is exceptionally bad. There is really no easy solution to this, since the public cannot be expected to tarry over the details of real causal models. But articles should at least acknowledge that many explanations for observed relationships may exist, and assuming causality is perilous.

2) Using graphics where tabular data is more revealing. I'd refer people to professor Tufte's works.

3) Gee-whiz animations and graphics. A good example is the rather inane simulation the NYT put together during the COVID epidemic showing dispersion of sneeze particles and the benefits of social distancing. Somebody spent a lot of time on that. But it really didn't explain anything at all. A graphic showing cone of dispersion, and showing that the area varies inversely with the square of distance (use a bar chart with the illustration,) that would explain and enlighten. Tell people that six feet is not only better than three feet, but a lot better than three feet.

4) Demented indices. Arbitrarily choosing and weighting variables to construct an index (Best state to retire, etc.) that ends up meaning nothing at all.

5) Rank-ordering things. This can be really stupid, especially when differences are trivial. And when measurements between observations being ranked derive from different sources.

I could go on all day, but the point is that a criticism section might, as I said, be helpful to practitioners.

TwoGunChuck (talk) 15:00, 2 September 2021 (UTC)[reply]

References

^ Coddington, Mark (7 November 2014). "Clarifying Journalism's Quantitative Turn". Digital Journalism. 3 (3): 331–348. doi:10.1080/21670811.2014.976400.

[1] Coddington, Mark (7 November 2014). "Clarifying Journalism's Quantitative Turn". Digital Journalism. 3 (3): 331–348. doi:10.1080/21670811.2014.976400.

[1]