Hi Fabian,

I think that the primary reason that articles with smaller byte counts show less consistency is due to templates.  A lot of stubs and starts are created with a collection of templates that consume few bytes of wikitext, but balloon into lots of HTML/content.  Regardless, there doesn't seem to be much cause for concern, so I saw the issue as resolved. 

FWIW, I originally showed up in this conversation because I was skeptical of your initial conclusion: "size in bytes is a really, really bad indicator for the actual, readable content of a Wikipedia article".  Now that we've worked out the strong correlation between wikitext length and readable content length for nearly all articles, I have little interest in looking into the data further.  

-Aaron

On Sat, Mar 15, 2014 at 12:47 PM, Floeck, Fabian (AIFB) <fabian.floeck@kit.edu> wrote:

Aaron, 

this seems kind of redundant as I already agreed that there is an overall high correlation and you posted this (almost) identical analysis 7 months ago. I don't know if you missed my later emails on the topic, but I already wrote that this "mistake" as you repeatedly put it, was a result of the selective sampling between 5000 and 6000 bytes. Hence, as I already said, my initial observations cannot be transferred to the general population of articles.

Not surprising and congruent with Aarons results, I also get a high linear correlation of 0.96 (random sample of 5000 articles) outside the 5800-6000 sample even if I filter out Disamb articles. 

But, as I as well explained, there seem to be some indicators that in smaller size articles, this correlation is not as strong. 

I split up the random 5000 article sample I posted last time at the median (3709 bytes) into two parts, each 2500 articles big. 

For the  "higher byte size" part (>3709 bytes) the correlation is 0.964
For the  "lesser byte size" part (<3710 bytes ) the correlation is only 0.295

You will of course not see that in your example if you just take all data (of all article sizes) and draw a straight regression line through them. The "blob" on the bottom left might need some further investigation. Maybe you could look at only articles under 5000, 3000, 1000 bytes and see if the correlation changes somehow. My guess is it will be less strong.

BTW: did you try to fit nonlinear models? 

I did not, and one reason for the bad fit in the lesser size articles could also be that there's a high correlation but not a linear one. 

Best, 

Fabian

 

On 04.08.2013, at 11:43, Aaron Halfaker <aaron.halfaker@gmail.com> wrote:

I just replicated this analysis.  I think you might have made some mistakes. 

I took a random sample of non-redirect articles from English Wikipedia and compared the byte_length (from database) to the content_length (from API, tags and comments stripped).

I get a pearson correlation coef of 0.9514766.

See the attached scatter plot including a linear regression line.  See also the regress output below. 

Call:

lm(formula = page_len ~ content_length, data = pages)

Residuals:

   Min     1Q Median     3Q    Max 

-38263   -419     82    592  37605 

Coefficients:

                Estimate Std. Error t value Pr(>|t|)    

(Intercept)    -97.40412   72.46523  -1.344    0.179    

content_length   1.14991    0.00832 138.210   <2e-16 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 2722 on 1998 degrees of freedom

Multiple R-squared: 0.9053, Adjusted R-squared: 0.9053 

F-statistic: 1.91e+04 on 1 and 1998 DF,  p-value: < 2.2e-16

 

On Fri, Aug 2, 2013 at 12:24 PM, Floeck, Fabian (AIFB) <fabian.floeck@kit.edu> wrote:

Hi,
to whoever is interested in this (and I hope I didn't just repeat someone else's experiments on this):

I wanted to know if a "long" or "short" article in terms of how much readable material (excluding pictures) is presented to the reader in the front-end is correlated to the byte size of the Wikisyntax which can be obtained from the DB or API; as people often define the "length" of an article by its length in bytes.

TL;DR: Turns out size in bytes is a really, really bad indicator for the actual, readable content of a Wikipedia article, even worse than I thought.

We "curl"ed the front-end HTML of all articles of the English Wikipedia (ns=0, no disambiguation, no redirects) between 5800 and 6000 bytes (as around 5900 bytes is the total en.wiki average for these articles). = 41981 articles.
Results for size in characters (w/ whitespaces) after cleaning the HTML out:
Min= 95 Max= 49441 Mean=4794.41 Std. Deviation=1712.748

Especially the gap between Min and Max was interesting. But templates make it possible.
(See e.g. "Veer Teja Vidhya Mandir School", "Martin Callanan" -- Allthough for the ladder you could argue that expandable template listings are not really main "reading" content..)

Effectively, correlation for readable character size with byte size = 0.04 (i.e. none) in the sample.

If someone already did this or a similar analysis, I'd appreciate pointers.

Best,

Fabian




--
Karlsruhe Institute of Technology (KIT)
Institute of Applied Informatics and Formal Description Methods

Dipl.-Medwiss. Fabian Flöck
Research Associate

Building 11.40, Room 222
KIT-Campus South
D-76128 Karlsruhe

Phone: +49 721 608 4 6584
Fax: +49 721 608 4 6580
Skype: f.floeck_work
E-Mail: fabian.floeck@kit.edu
WWW: http://www.aifb.kit.edu/web/Fabian_Flöck

KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association


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-- 

Dipl.-Medwiss. Fabian Flöck
Research Associate

Karlsruhe Institute of Technology (KIT)
Institute of Applied Informatics and Formal Description Methods

Building 11.40, Room 222
KIT-Campus South
D-76128 Karlsruhe

Phone: +49 721 608 4 6584

Fax: +49 721 608 4 6580
Skype: f.floeck_work

E-Mail: floeck@kit.edu

WWW: http://www.aifb.kit.edu/web/Fabian_Flöck

KIT – University of the State of Baden-Wuerttemberg and
National Research Center of the Helmholtz Association

_______________________________________________
Wiki-research-l mailing list
Wiki-research-l@lists.wikimedia.org
https://lists.wikimedia.org/mailman/listinfo/wiki-research-l

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