For media files there are two things we already have, size of the file and who created it.

Filesize doesn't guarantee quality, but there must be a size below which we just have a blurry thumbnail.

Assessing the possible quality of images by looking at how many other quality images have come from the same user is likely to be helpful. Another image by someone who has had multiple featured pictures should be a positive on any rating system.

We now have hundreds of thousands of images that are categorised as being of various important objects such as listed buildings in the UK.

Other variables we could consider include the camera used, again a good camera with the settings mucked up is likely to take a worse photograph than a cheap camera in the hands of someone who knows how to coax the bet out of it.


Several other things may or may not be possible, though if there isn't already open sourced software that can do it it could be expensive to create it: Technology exists out there that can compare different human faces, so it should be possible to write software that classifies images as blurred, washed out or poorly lit. Also we could have software that identifies near duplicates and in some fashion threads similar images behind the "best" one.


The AFT was always vulnerable to just creating extra work for the community and diverting people from possibly improving articles to asking others to fix them. But software could be written that encouraged rather than undermined the SoFixIt culture of our heyday. If we had an app that invited people to check "Is this still the best image to illustrate this Wikipedia article?" Then we could feed it with articles where we believed we had more images on commons than we were using, or where the same image had been used for several years, or the image used was small or otherwise suspect, or indeed where we didn't have an image. Leutha and I ran a couple of sessions a year or so back showing donors how to add images to articles, and we found that  even people who were quite hesitant about editing were very confident deciding which of several images could be used to illustrate an article, even if they had never been to the village or river in question. As well as improving articles and acting as a much needed new entrypoint for editors, this could give us another metric on media quality - "one of x images considered but not used to illustrate the article on that subject" or indeed "image z was replaced in article x by image y" which should usually mean that image y is higher "quality" than image z.


Some sort of weighted score that combines the above could be what we need, though of course many of the criteria are subjective and scores could drop over time as better media files are uploaded.




Jonathan Cardy
GLAM (Galleries, Libraries, Archives & Museums) Organiser/Trefnydd GLAM (Galeriau, Llyfrgelloedd, Archifdai a llawer Mwy!)
Wikimedia UK
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On 16 April 2014 19:53, Charles Matthews <charles.r.matthews@ntlworld.com> wrote:
On 16 April 2014 19:28, Simon Knight <sjgknight@gmail.com> wrote:

Thanks Dan, to be clear, the proposal is not to develop another manual rating system (such as the AFT or the project rating systems), it’s to develop some automated quality assessments. Those might include some manual elements as inputs particularly for any machine learning approach, but generating new methods there is not the aim of the project.

 


There's the old DREWS acronym from How Wikipedia Works. to which I'd now add T for traffic. In other words there are six factors that an experienced human would use to analyse quality, looking in particular for warning signs.

D = Discussion: crunch the talk page (20 archives = controversial, while no comments indicates possible neglect)
R = WikiProject rating, FWIW, if there is one. 
E = Edit history. A single editor, or essentially only one editor with tweaking, is a warning sign. (Though not if it is me, obviously)
W = Writing. This would take some sort of text analysis. Work to do here. Includes detection of non-standard format, which would suggest neglect by experienced editors.
S = Sources. Count footnotes and so on.
T = Traffic. Pages at 100 hits per month are not getting many eyeballs. Warning sign. Very high traffic is another issue.

Seems to me that there is enough to bite on, here. 

Charles

 

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