This paper (first reference) is the result of a class project I was part of
almost two years ago for CSCI 5417 Information Retrieval Systems. It builds
on a class project I did in CSCI 5832 Natural Language Processing and which
I presented at Wikimania '07. The project was very late as we didn't send
the final paper in until the day before new years. This technical report was
never really announced that I recall so I thought it would be interesting to
look briefly at the results. The goal of this paper was to break articles
down into surface features and latent features and then use those to study
the rating system being used, predict article quality and rank results in a
search engine. We used the [[random forests]] classifier which allowed us to
analyze the contribution of each feature to performance by looking directly
at the weights that were assigned. While the surface analysis was performed
on the whole english wikipedia, the latent analysis was performed on the
simple english wikipedia (it is more expensive to compute). = Surface
features = * Readability measures are the single best predictor of quality
that I have found, as defined by the Wikipedia Editorial Team (WET). The
[[Automated Readability Index]], [[Gunning Fog Index]] and [[Flesch-Kincaid
Grade Level]] were the strongest predictors, followed by length of article
html, number of paragraphs, [[Flesh Reading Ease]], [[Smog Grading]], number
of internal links, [[Laesbarhedsindex Readability Formula]], number of words
and number of references. Weakly predictive were number of to be's, number
of sentences, [[Coleman-Liau Index]], number of templates, PageRank, number
of external links, number of relative links. Not predictive (overall - see
the end of section 2 for the per-rating score breakdown): Number of h2 or
h3's, number of conjunctions, number of images*, average word length, number
of h4's, number of prepositions, number of pronouns, number of interlanguage
links, average syllables per word, number of nominalizations, article age
(based on page id), proportion of questions, average sentence length. :*
Number of images was actually by far the single strongest predictor of any
class, but only for Featured articles. Because it was so good at picking out
featured articles and somewhat good at picking out A and G articles the
classifier was confused in so many cases that the overall contribution of
this feature to classification performance is zero. :* Number of external
links is strongly predictive of Featured articles. :* The B class is highly
distinctive. It has a strong "signature," with high predictive value
assigned to many features. The Featured class is also very distinctive. F, B
and S (Stop/Stub) contain the most information.
:* A is the least distinct class, not being very different from F or G. =
Latent features = The algorithm used for latent analysis, which is an
analysis of the occurence of words in every document with respect to the
link structure of the encyclopedia ("concepts"), is [[Latent Dirichlet
Allocation]]. This part of the analysis was done by CS PhD student Praful
Mangalath. An example of what can be done with the result of this analysis
is that you provide a word (a search query) such as "hippie". You can then
look at the weight of every article for the word hippie. You can pick the
article with the largest weight, and then look at its link network. You can
pick out the articles that this article links to and/or which link to this
article that are also weighted strongly for the word hippie, while also
contributing maximally to this articles "hippieness". We tried this query in
our system (LDA), Google (site:en.wikipedia.org hippie), and the Simple
English Wikipedia's Lucene search engine. The breakdown of articles occuring
in the top ten search results for this word for those engines is: * LDA
only: [[Acid rock]], [[Aldeburgh Festival]], [[Anne Murray]], [[Carl
Radle]], [[Harry Nilsson]], [[Jack Kerouac]], [[Phil Spector]], [[Plastic
Ono Band]], [[Rock and Roll]], [[Salvador Allende]], [[Smothers brothers]],
[[Stanley Kubrick]]. * Google only: [[Glam Rock]], [[South Park]]. * Simple
only: [[African Americans]], [[Charles Manson]], [[Counterculture]], [[Drug
use]], [[Flower Power]], [[Nuclear weapons]], [[Phish]], [[Sexual
liberation]], [[Summer of Love]] * LDA & Google & Simple: [[Hippie]],
[[Human Be-in]], [[Students for a democratic society]], [[Woodstock
festival]] * LDA & Google: [[Psychedelic Pop]] * Google & Simple: [[Lysergic
acid diethylamide]], [[Summer of Love]] ( See the paper for the articles
produced for the keywords philosophy and economics ) = Discussion /
Conclusion = * The results of the latent analysis are totally up to your
perception. But what is interesting is that the LDA features predict the WET
ratings of quality just as well as the surface level features. Both feature
sets (surface and latent) both pull out all almost of the information that
the rating system bears. * The rating system devised by the WET is not
distinctive. You can best tell the difference between, grouped together,
Featured, A and Good articles vs B articles. Featured, A and Good articles
are also quite distinctive (Figure 1). Note that in this study we didn't
look at Start's and Stubs, but in earlier paper we did. :* This is
interesting when compared to this recent entry on the YouTube blog. "Five
Stars Dominate Ratings"
I think a sane, well researched (with actual subjects) rating system
well within the purview of the Usability Initiative. Helping people find and
create good content is what Wikipedia is all about. Having a solid rating
system allows you to reorganized the user interface, the Wikipedia
namespace, and the main namespace around good content and bad content as
needed. If you don't have a solid, information bearing rating system you
don't know what good content really is (really bad content is easy to spot).
:* My Wikimania talk was all about gathering data from people about articles
and using that to train machines to automatically pick out good content. You
ask people questions along dimensions that make sense to people, and give
the machine access to other surface features (such as a statistical measure
of readability, or length) and latent features (such as can be derived from
document word occurence and encyclopedia link structure). I referenced page
262 of Zen and the Art of Motorcycle Maintenance to give an example of the
kind of qualitative features I would ask people. It really depends on what
features end up bearing information, to be tested in "the lab". Each word is
an example dimension of quality: We have "*unity, vividness, authority,
economy, sensitivity, clarity, emphasis, flow, suspense, brilliance,
precision, proportion, depth and so on.*" You then use surface and latent
features to predict these values for all articles. You can also say, when a
person rates this article as high on the x scale, they also mean that it has
has this much of these surface and these latent features.
= References =
- DeHoust, C., Mangalath, P., Mingus., B. (2008). *Improving search in
Wikipedia through quality and concept discovery*. Technical Report.
- Rassbach, L., Mingus., B, Blackford, T. (2007). *Exploring the
feasibility of automatically rating online article quality*. Technical
I have asked and received permission to forward to you all this most
excellent bit of news.
The linguist list, is a most excellent resource for people interested in the
field of linguistics. As I mentioned some time ago they have had a funding
drive and in that funding drive they asked for a certain amount of money in
a given amount of days and they would then have a project on Wikipedia to
learn what needs doing to get better coverage for the field of linguistics.
What you will read in this mail that the total community of linguists are
asked to cooperate. I am really thrilled as it will also get us more
linguists interested in what we do. My hope is that a fraction will be
interested in the languages that they care for and help it become more
relevant. As a member of the "language prevention committee", I love to get
more knowledgeable people involved in our smaller projects. If it means that
we get more requests for more projects we will really feel embarrassed with
all the new projects we will have to approve because of the quality of the
Incubator content and the quality of the linguistic arguments why we should
approve yet another language :)
NB Is this not a really clever way of raising money; give us this much in
this time frame and we will then do this as a bonus...
---------- Forwarded message ----------
From: LINGUIST Network <linguist(a)linguistlist.org>
Date: Jun 18, 2007 6:53 PM
Subject: 18.1831, All: Call for Participation: Wikipedia Volunteers
LINGUIST List: Vol-18-1831. Mon Jun 18 2007. ISSN: 1068 - 4875.
Subject: 18.1831, All: Call for Participation: Wikipedia Volunteers
Moderators: Anthony Aristar, Eastern Michigan U <aristar(a)linguistlist.org>
Helen Aristar-Dry, Eastern Michigan U <hdry(a)linguistlist.org>
Reviews: Laura Welcher, Rosetta Project
The LINGUIST List is funded by Eastern Michigan University,
and donations from subscribers and publishers.
Editor for this issue: Ann Sawyer <sawyer(a)linguistlist.org>
To post to LINGUIST, use our convenient web form at
From: Hannah Morales < hannah(a)linguistlist.org >
Subject: Wikipedia Volunteers
-------------------------Message 1 ----------------------------------
Date: Mon, 18 Jun 2007 12:49:35
From: Hannah Morales < hannah(a)linguistlist.org >
Subject: Wikipedia Volunteers
As you may recall, one of our Fund Drive 2007 campaigns was called the
"Wikipedia Update Vote." We asked our viewers to consider earmarking their
donations to organize an update project on linguistics entries in the
English-language Wikipedia. You can find more background information on this
The speed with which we met our goal, thanks to the interest and generosity
our readers, was a sure sign that the linguistics community was enthusiastic
about the idea. Now that summer is upon us, and some of you may have a bit
leisure time, we are hoping that you will be able to help us get started on
Wikipedia project. The LINGUIST List's role in this project is a purely
organizational one. We will:
*Help, with your input, to identify major gaps in the Wikipedia materials or
pages that need improvement;
*Compile a list of linguistics pages that Wikipedia editors have identified
"in need of attention from an expert on the subject" or " does not cite any
references or sources," etc;
*Send out periodical calls for volunteer contributors on specific topics or
*Provide simple instructions on how to upload your entries into Wikipedia;
*Keep track of our project Wikipedians;
*Keep track of revisions and new entries;
*Work with Wikimedia Foundation to publicize the linguistics community's
We hope you are as enthusiastic about this effort as we are. Just to help us
get started looking at Wikipedia more critically, and to easily identify an
needing improvement, we suggest that you take a look at the List of
Many people are not listed there; others need to have more facts and
added. If you would like to participate in this exciting update effort,
respond by sending an email to LINGUIST Editor Hannah Morales at
hannah(a)linguistlist.org, suggesting what your role might be or which
entries you feel should be updated or added. Some linguists who saw our
on the Internet have already written us with specific suggestions, which we
share with you soon.
This update project will take major time and effort on all our parts. The
result will be a much richer internet resource of information on the breadth
depth of the field of linguistics. Our efforts should also stimulate
students to consider studying linguistics and to educate a wider public on
we do. Please consider participating.
Editor, Wikipedia Update Project
Linguistic Field(s): Not Applicable
LINGUIST List: Vol-18-1831
I was asked by a volunteer for help getting stats on the gender gap in
content on a certain Wikipedia, and came up with simple Wikidata Query
Service queries that pulled the total number of articles on a given
Wikipedia about men and about women, to calculate *the proportion of
articles about women out of all articles about humans*.
Then I was curious about how that wiki compared to other wikis, so I ran
the queries on a bunch of languages, and gathered the results into a table,
(please see the *caveat* there.)
I don't have time to fully write-up everything I find interesting in those
results, but I will quickly point out the following:
1. The Nepali statistic is simply astonishing! There must be a story
there. I'm keen on learning more about this, if anyone can shed light.
2. Evidently, ~13%-17% seems like a robust average of the proportion of
articles about women among all biographies.
3. among the top 10 largest wikis, Japanese is the least imbalanced. Good
job, Japanese Wikipedians! I wonder if you have a good sense of what
drives this relatively better balance. (my instinctive guess is pop culture
4. among the top 10 largest wikis, Russian is the most imbalanced.
5. I intend to re-generate these stats every two months or so, to
eventually have some sense of trends and changes.
6. Your efforts, particularly on small-to-medium wikis, can really make a
dent in these numbers! For example, it seems I am personally
responsible for almost 1% of the coverage of women on Hebrew Wikipedia!
7. I encourage you to share these numbers with your communities. Perhaps
you'd like to overtake the wiki just above yours? :)
8. I'm happy to add additional languages to the table, by request. Or you
can do it yourself, too. :)
 Yay #100wikidays :) https://meta.wikimedia.org/wiki/100wikidays
Wikimedia Foundation <http://www.wikimediafoundation.org>
Imagine a world in which every single human being can freely share in the
sum of all knowledge. Help us make it a reality!
Being put together by Eliezer Yudkowsky of LessWrong. Content is
cc-by-sa 3.0, don't know about the software.
Rather than the "encyclopedia" approach, it tries to be more
pedagogical, teaching the reader at their level.
Analysis from a sometime Yudkowsky critic on Tumblr:
(there's a pile more comments linked from the notes on that post,
mostly from quasi-fans; I have an acerbic comment in there, but you
should look at the site yourself first.)
No idea if this will go anywhere, but might be of interest; new
approaches generally are. They started in December, first publicised
it a week ago and have been scaling up. First day it collapsed due to
load from a Facebook post announcement ... so maybe hold off before
announcing it everywhere :-)
(this is an announcement in my capacity as a volunteer.)
Inspired by a lightning talk at the recent CEE Meeting by our colleague
Lars Aronsson, I made a little command-line tool to automate batch
recording of pronunciations of words by native speakers, for uploading to
Commons and integration into Wiktionary etc. It is called *pronuncify*, is
written in Ruby and uses the sox(1) tool, and should work on any modern
Linux (and possibly OS X) machine. It is available here, with
I was then asked about a Windows version, and agreed to attempt one. This
version is called *pronuncify.net <http://pronuncify.net>*, and is a .NET
gooey GUI version of the same tool, with slightly different functions. It
is available here, with instructions.
Both tools require word-list files in plaintext, with one word (or phrase)
per line. Both tools name the files according to the standard established
in [[commons:Category:Pronunciation]], and convert them to Ogg Vorbis for
you, so they are ready to upload.
In the future, I may add OAuth-based direct uploading to Commons. If you
run into difficulties, please file issues on GitHub, for the appropriate
tool. Feedback is welcome.
Hello all. I had a positive experience at WikiConference North America
last weekend, where I gave a talk on transgender issues and
Wikipedia. I'm posting because there's an active discussion in
Wikipedia Weekly on Facebook about choosing a host country for Wikimania
2018. I am concerned that some of the suggestions are not taking into
account the safety of LGBT+ people; not just those attending the
conference, but also those living in the host country.
As a queer trans atheist in a same-sex marriage, there are a number of
places where I am considered a criminal for just existing or going about
my daily routine. This applies to some parts of the U.S. as well, by the
way; I won't be visiting North Carolina as long as it's illegal for me
to use the men's restroom there. Please keep these considerations in
mind when planning meetups and conferences. Thank you.
- Pax aka Funcrunch
Pax Ahimsa Gethen | http://funcrunch.org
Below are some quotes from past donors to WMF, courtesy of Megan Hernandez.
I appreciated reading these in 2014 and thought that others might
appreciate them again also:
--I consider Wikipedia one of the few (the only?) big internet company who
are actually trustworthy. I value Wikipedia and I want to support it
--I read a comic about a Wikipedia-Comcast merger. It was terrifying.
--Keep doing what you are doing. I love Wikipedia. My most visited site by
a mile. Any time I watch a new documentary, nature show, or read a book
about a topic I find fascinating, I always Wikipedia the information.
--Very helpful! As a nursing student I frequently use Wikipedia to
reference science and engineering topics so I can get a deeper
understanding concepts and extra learning. Ilove it. As a young child in
the early 1960s I thought that computers were going to be giant machines
you could ask any question and get answers, Wikipedia is that machine! It's
wonderful! Just sharing.
--It can finish a debate in a couple of clicks
--Once upon a time far far away I wanted a set of encyclopedias, but, alas
I could not afford them. Wikipedia now fills that void.
--I visit and utilize Wikipedia multiple times every week, sometimes daily.
Going to Wikipedia has become second nature to me. It is synonymous with
knowledge - there's nothing more profound. I support it with the small
donations I can afford in the hope of setting an example, and so that
others may have the same opportunities I've had.
--There have been so many ways that the internet has disappointed me in my
hopes that it would improve the human condition. However, there is this one
shining exception, and from my least expected source. Wikipedia is that
best thing that humanity has done with the internet, contributing to both a
common knowledge set and re-learning how to find areas of agreement with
others, instead of just shouting and not listening.
--Most often I turn to Wiki in order to answer a question that my grandson
has asked about life, the universe and everything...in the old days we had
a kids' encyclopedia on the bookshelf, but it became outdated and Wiki has
taken its place.
--Knowledge is the key to so many locks. Thank you
We are coming up to that time of year again with the launch of our English
fundraiser. Our E-mail campaign is already underway and in a little under
three weeks time, the banner campaign will launch in the US, UK, Canada,
Australia, New Zealand and Ireland on Giving Tuesday , November 29,
2016. We will continue to try and limit the disruption from these banners.
Our current expectation is to run our banners for all traffic for the first
two weeks. Following that we will some combination of either reducing the
amount of traffic being shown banners or the number of times a banner is
shown to each user. There will then be one last final push before the end
of December. It is my hope to update you after both of these stages with
It is certainly no secret that it is a very important period for
fundraising as our December activities are responsible for raising around
45% of all movement funds. As we reported in last years fundraising report
 and at the September metrics meeting  we continue to adapt to the
shift in our readership from desktop to mobile. Over the last two years,
our e-mail efforts have played an increasingly major role in our
fundraising to counter this shift and will certainly be the case over the
next two months.
As always it’s critical for my team to have both broader staff and
community input in our fundraising efforts. This year we have been working
closely with the Reading product team along with members from both the
Reading and Editing design teams to improve our fundraising flow, in
particular, trying to keep closer to the new standardised Wikimedia UI
guidelines . In addition to this, over the last five months we ran a
number of staff and community feedback sessions and we have been very
grateful to everyone who took part in those. They proved very successful in
providing both a constructive critical eye for existing banner and email
appeals as well being a source for a plethora of new ideas.
The plan is that we will run more of these in conjunction with some of our
major campaigns throughout the year. This will start with a series of
sessions focusing on the English Campaign and would like to invite you all
to a number of session being run over the next two weeks:
* Thursday 17th November @ 1300 UTC
* Thursday 17th November @ 1900 UTC
* Monday 21st November @ 0100 UTC
Please do sign up and find out more information . Participation will be
via IRC, Youtube live & via Google Hangout for ease of participation.
As always if you have ideas and are not able to participate in these
sessions you can leave feedback on our Fundraising Ideas page where you can
see links to our current fundraising banners and current appeal text .
Over the last year: use of Phabricator  for bug reporting; event and
related content specific banners; improving the ease with which to dismiss
banners; numerous improvements to the language used; and country specific
images all came about from suggestions made on that page. So please do keep
the ideas coming and I would like to thank you all in advance both for your
input into the campaigns but more importantly the awesome work in building
one of the largest sources of freely accessible knowledge in human history.
I look forward to working with you all in the coming weeks.
*Advancement Associate (Community Engagement)*
 2015-2016 WMF Fundraising Report:
 September 2016 Metrics Meeting Presentation:
 Wikimedia Design Guide
Color palettes https://phabricator.wikimedia.org/M82
Collection of widgets: https://phabricator.wikimedia.org/M101
Demo widgets in OOjs UI:
 To sign up for a feedback session -
 To suggest new banners ideas visit the test ideas meta page -
 To file a bug report or technical issue, please create a phabricator
Dear fellow Wikimedians,
Over the weekend, Wikimedia Deutschland held its General Assembly and
elections for the supervisory board as well as for the auditors.
The newly elected board consists of seven members:
* Tim Moritz Hector (Chair, re-elected)
* Sebastian Moleski (Treasurer, re-elected)
* Sabria David (re-elected)
* Kurt Jansson (re-elected)
* Lukas Mezger (re-elected)
* Harald Krichel (re-elected)
* Johanna Niesyto
The board has met for its first meeting on Sunday and elected Sabria David
and Kurt Jansson as Deputy Chair.
Johanna is new to the board, so please join me in welcoming her to the
board of WMDE. I would also like to thank our former board members Nikolas
Becker, Jürgen Friedrich and Catrin Schoneville wholeheartedly for the
great work and their commitment to Wikimedia Deutschland in the past years.
Lena Stammler and Daniel Baur (re-elected) are our two recently-elected
auditors, and I am extending my congratulations to them.
At the GA, the board furthermore presented the final report on the
WMDE-Governance-Review and how we handled the recommendations of the
report. Moreover, the members approved the annual plan for 2017.
The board will meet in January to discuss the goals for our term in the
next two years and I am very much looking forward to working together with
Tim Moritz Hector
Tim Moritz Hector
Chair of the Board
Wikimedia Deutschland e. V.