Hoi Gerard,
On Mon, Apr 17, 2017 at 7:54 AM, Gerard Meijssen <gerard.meijssen(a)gmail.com>
wrote:
When you analyse articles and find that some things are missing, it will
help a lot when you can target these articles to the people who are likely
interested. When people interested in soccer learn that a soccer player
died, they are more likely to edit even write an article.
You are absolutely right. This is what we even tested in the article
creation recommendation experiment and you could see that providing
personalized recommendations (where personalization was on the basis of
matching editors interests based on their history of contributions) does
better than random important recommendations. A few pointers for you:
* Check out section 2.3 of the paper at
https://arxiv.org/pdf/1604.03235.pdf to
see how this was done.
* I talk briefly about how we do the editor interest modeling at
https://youtu.be/lHbnvRwFC_A?t=20m44s
In general, we have at least two ways for recommending to people what they
like to edit: one would be using the information in their past edit history
and building topical models that can help us learn what topics an editor is
interested in. The other is by asking the editor to provide some seeds of
interest to us. For example, we ask you to tell us what kind of article you
would edit, and we give you recommendations similar to the seed you
provide. Each have its own advantages and you sometimes have to mix the two
approaches (and more) to give the editor enough breadth and depth of topics
to choose from.
The approach for finding a subject that could do with
more attention is one
I applaud. When you want to do this across languages think Wikidata to
define the area of interest for users. It will always include all the
articles in all the languages. As you have seen with the Listeria lists,
showing red links and Wikidata items is trivial.
Yes, finding what is missing in a Wikipedia language by comparing language
editions is relatively easy, thanks to Wikidata. :) What is hard is ranking
these millions of missing articles in any language based on some notion of
importance. We developed a ranking system for the research I mentioned
above. You can read about it in Section 2.2 of the paper at
https://arxiv.org/pdf/1604.03235.pdf. I talk about in less details at
https://youtu.be/lHbnvRwFC_A?t=16m58s. In a nutshell: we built a prediction
model that aims to predict the number of pageviews the article would
receive had it existed in the destination language where it's missing
today. The higher this predicted number for a missing article in a
language, the more important it is to create it.
Best,
Leila
Thanks,
Gerard
On 17 April 2017 at 02:04, Leila Zia <leila(a)wikimedia.org> wrote:
Hi John,
This may be of interest to you:
We are working on building recommendation systems than can help editors
identify how to expand already existing articles in Wikipedia. This
includes but is not limited to identifying what sections are missing from
an article, what citations, what images, infobox information, etc. This
is
research in its early days, if you'd like to
follow up with it please
visit
Wikipedia_stubs_across_
languages
Best,
Leila
Leila Zia
Senior Research Scientist
Wikimedia Foundation
On Sat, Apr 15, 2017 at 2:50 PM, John Erling Blad <jeblad(a)gmail.com>
wrote:
> Are anyone doing any work on automated quality assurance of articles?
Not
> the ORES-stuff, that is about creating hints
from measured features.
I'm
thinking
about verifying existence and completeness of citations, and
structure of logical arguments.
John
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