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
>>> The people who are loudest in their demands for consensus
>>> do not represent the Wikimedia movement.
>> The voices loudest for the WMF doing something against the
>> Trump administration are not representative of the Wikimedia
>> movement either....
> Is the Community Process Steering Committee currently
> prepared to "engage more 'quiet' members of our community"
> with a statistically robust snap survey to resolve this question?
Anyone can go to Recent Changes and send a SurveyMonkey link to the
most recent few hundred editors with contributions at least a year
old, to get an accurate answer.
Will a respected member of the community please do this? I would like
to know what the actual editing community thinks of the travel ban and
their idea of an appropriate response. I don't want to see community
governance by opt-in participation in obscure RFCs.
I would offer to do this myself, but I value keeping my real name
unassociated with my enwiki userid.
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!
In November 2016, I presented the result of a joint research that
helped us understand English Wikipedia readers better. (Presentation
at https://www.youtube.com/watch?v=xIaMuWA84bY ). I talked about how
we used English, Persian, and Spanish Wikipedia readers' inputs to
build a taxonomy of Wikipedia use-cases along several dimensions,
capturing users’ motivations to visit Wikipedia, the depth of
knowledge they are seeking, and their knowledge of the topic of
interest prior to visiting Wikipedia. I also talked about the results
of the study we did to quantify the prevalence of these use-cases via
a large-scale user survey conducted on English Wikipedia. In that
study, we also matched survey responses to the respondents’ digital
traces in Wikipedia’s server logs which enabled us in discovering
behavioral patterns associated with specific use-cases. You can read
the full study at https://arxiv.org/abs/1702.05379 .
==What do we want to do now?==
There are quite a few directions this research can continue on, and
the most immediate one is to understand whether the results that we
observe (in English Wikipeida) is robust across languages/cultures.
For this, we are going to repeat the study, but this time in more
languages. Here are the languages on our list: Arabic, Dutch, English,
Hindi, Japanese, Spanish (thanks to all the volunteers who have been
helping us translating all survey related documents to these
==What about your language?==
If your language is not one of the six languages above and you'd like
to learn about the readers of Wikipedia in it (in the specific ways
described above), please get back to me by Monday, April 24, AoE. I
cannot guarantee that we can run the study in your language, however,
I guarantee that we will give it a good try if you're interested. The
decision to include more languages will depend on: our capacity to do
the analysis, the speed at which your community can help us translate
the material to the language, the traffic to that language, a couple
of sentences on how you'd think the result can help your community,
and your willingness to help us document the results for your language
(Quite some work will need to go to have readable/usable
documentations available and we are too small to be able to guarantee
that on our own for many languages.)
Senior Research Scientist
The Foundation has been accepting BitCoin donations. Unfortunately,
BitCoin is very wasteful in terms of electricity, and is therefore a
I recommend that the Foundation immediately cease accepting BitCoin,
and require donors who wish to donate in cryptocurrency to convert to
FoldingCoin instead. Please see: FoldingCoin (FLDC)
This conversion will place the Foundation at the forefront of
cryptocurrency technology, and stop it from contributing to extremely
dirty waste. As other cryptocurrencies based on proofs of useful work
instead of useless work emerge, the Foundation should consider those.
FoldingCoin is based on proofs of useful prediction of protein
folding, which is useful for computer-aided antibody design, and used
in turn for cancer therapies and many other applied and research
I also invite anyone in the community interested in co-authoring my
forthcoming derivative whitepaper on proofs of useful intelligibility
remediation work to contact me off-list, please. I am also willing to
help with proofs of useful encyclopedia article improvement, but I am
not certain if ORES is yet robust enough to support such proof in a
I have been looking for social networking service that would be fair: not abusing personal data, funded by community, respecting privacy, accepting anonymity, free/libre/ open source etc. Haven’t found many. The Diaspora* Project is not moving forward very fast and the Mastodon is more a microblogging service rather than a social network service.
Would it make sense for Wikimedia movement to build its own social network service?
In the "2017 Movement strategy” we state: “By 2030, Wikimedia will become the essential infrastructure of the ecosystem of free knowledge”. If we consider discussions and information shared on social network services to be “knowledge”, I think we should have a role in here too.
We have 33 million registered users and fulfil all the requirements of being a “fair service”. A minimum list of features to make Wikimedia Social would be:
(1) Status updates
I am pretty sure that by integrating this to other Wikimedia services (Commons etc.) we could achieve something awesome.
I'm delighted to announce that the Wikimedia Foundation's Annual Plan for
FY18-19 is now on Meta.
This year, we have organized our efforts around three goals that focus on
making critical improvements to our systems and structures to ensure that
we’re better positioned for our coming work against the strategic
direction. The Foundation’s goals for this year should not only move us
closer to knowledge equity and service, but will prepare us to execute
against the 3- to 5-year strategic plan which we intend to develop this
year in order to guide the Foundation’s work into the future.
As you’ll see, we’ve made some changes to the structure of this year’s
annual plan. This year’s plan is organized around three goals for the
Foundation’s work in the year to come. By restructuring the Annual Plan, we
have written a plan for the whole Foundation, rather than an aggregation
of plans from all of our departments and teams. In this sense, we’re
seeking to become a better-integrated institution, rather than a collection
of teams and departments with disparate goals.
We’ve also reduced the overall length of the published Annual Plan. We
wanted to make sure that the focus and goals of our work don’t get lost in
the details. Of course, we know that many community members enjoy reading
the particulars of our planned work, so you can still access the details of
departmental programs through links to their descriptions on Meta or
MediaWiki.org. These links will provide interested readers with detailed
departmental programs, which describe the specific and detailed program
goals, impact and outcomes. This change does not sacrifice the depth and
rigor of our planning process, but rather, it is meant to keep the Annual
Plan lean and focused while allowing interested readers to dive deep into
Finally, we’ve expanded the planning framework we instituted last year for
cross-departmental programs to all of our programs across the Foundation.
This allows us to clearly link a program’s resources to outcomes and
measures. As such, we’ve presented the Annual Plan budget in terms of our
investments in the three defined goals rather than in terms of our internal
Thank you all for your support over the past year. I'm really looking
forward to your feedback on this year's proposed plan during the open
comment period -- a reminder it runs through May 15th.
1 Montgomery Street, Suite 1600
San Francisco, CA 94104
+1 (415) 839-6885 ext. 6635 <(415)%20839-6885>
+1 (415) 712 4873 <(415)%20712-4873>
According to their twitter feed they have announced a partnership with
something called the "Request Network" for cryptocurrency donations.
Also this article here
Ok. I don't approve but I'm not french so not its not an area where I
can reasonably expect anyone to pay any attention to my opinions.
What concerns me is that they have retweeted something claiming the
partnership is with the wikimedia foundation rather than just
Is some form of clarification possible?