On Thu, Jun 3, 2010 at 4:14 PM, Reid Priedhorsky <reid@umn.edu> wrote:

Brian J Mingus wrote:
> ---------- Forwarded message ----------
> From: Brian <Brian.Mingus@colorado.edu>
> Date: Wed, Jun 2, 2010 at 10:46 PM
> Subject: Re: [Wiki-research-l] Quality and pageviews
> To: Liam Wyatt <liamwyatt@gmail.com>
>
>
> Interestingly, the result is negative. The correlation coefficient between
> 2500 featured articles and 2500 random articles is .18 which is very low. I
> also trained a linear classifier to predict the quality of an article based
> on the number of page views and it was no better than chance.

That reminds me of an incidental finding from our 2007 work: we wanted
to use article edit rate to predict view rate, but there was no
correlation between the two.

Reid

That is an interesting negative finding as well. Just so this thread doesn't go without some positive results, here is a table from one of my technical reports on some features that do correlate with quality. If the number is greater than zero it correlates with quality, if it is 0 it does not correlate, and if it is less than 0 it is negatively correlated with quality. The scale of the numbers is meaningless and not interpretable, although the relative magnitude is important. These are just the relative performance of each feature for each class, as extracted from the weights of a random forests classifier.

http://grey.colorado.edu/mediawiki/sites/mingus/images/1/1e/DeHoustMangalathMingus08_feature_table.png

Summary (features in order of predictive ability):

Featured articles are correlated with Number of images, Number of external links, Automated Readability Index, Number of references, Number of internal links, Length of article HTML, Gunning Fog Index, Flesch-Kincaid Grade Level, Lesbarhedsindex Readability Formula, Number of words, Number of to be's, Number of sentences

Note that featured articles are easy to predict.

A articles are correlated with Number of references, PageRank, Number of external links, Number of images, Article age (page-id).

Note that A articles are extremely hard to predict. All of the above A predictors are weaker than all of the featured predictors. This class should be merged with another quality class.

G articles are correlated with Number of external links, Number of templates, Number of references, Automated Readability Index, Flesh-Kincaid Grade Level
G articles are negatively correlated with Length of article HTML, Flesch Reading Ease, Smog Grading

Note that G articles are extremely hard to predict and should be merged with another quality class.

B articles are correlated with Automated Readability Index, Flesch-Kincaid Grade Level, Laesbarhedsindex Readability Formula, Gunning Fog Index, Length of Article HTML, Number of paragraphs, Flesh Reading Ease, Smog Grading, Number of internal links, Number of words, Number of references, Number of to be's, Number of sentences, Coleman-Liau Index, Number of templates, PageRank, Number of external links, Number of relative links, Number of <h3>s, Number of interlanguage links

Note that B articles are very easy to predict.

Start/Stub were left out of this analysis because they are so easy to predict based on a lack of pretty much any useful information.