Some thoughts that come to mind while (and after) reading Derek Willis’s post, Buying Into Computational Journalism.
(1) Even though computational journalism (like CAR and even investigative journalism) is a rather small offshoot of the tribe, an understanding of the techniques involved should be as widely known as possible by all journalists.
(2) We need to find ways to more tightly integrate social sciences and teaching journalism. One of the things that mean is that the social sciences, particularly at the higher levels, are going to have to get a little less inward-looking.
(3) We need to deal with the sometimes abysmal ignorance of basic math, and the attitude in journalism that once had me half-convinced I could make a killing with a T-shirt that read: I’m a Journalist. I don’t do math.
(4) In training journalists, and in providing basic media literacy for citizens, we need to teach people how to understand not only statistics and data as presented, but statistics as method for analyzing data. I’m not talking about the deep knowledge that a math or sociology major needs, but about a firm grasp of methods, strengths, weaknesses, etc. (I’ve argued in our department that we should require first students to take a first-year statistics course. The reaction has been, from some faculty, cool.)
(5) I was particularly taken by one of the comments on Derek’s piece, which I think describes a weakness in the way we are (so far) taking advantage of the wealth of suddenly-available data:
I, for one, am more looking forward to the sense-making tools aspect, which the report unjustly separating from pattern discovery. I see too many journalists around me talking about searching data sources for the handful of useful nuggets within, but the value is in making sense of the whole thing, which means grown-up no-black-and-white-answers statistics if you take this seriously. It’s as if everyone were trying to dig a water well in the middle of a lake.
Please note that these are thoughts that have come to mind, not definitive conclusions. I’ve said before that a lot of what happens here is me thinking out loud while I work toward conclusions. I’d be interested in comments any of you have.