Hello research list,
Can I draw your attention to this mail?
I'm not sure what Leigh is waiting for or what form of approval she
might need, but I'm hoping somebody on this list could get in touch
with her to help her figure it out. Melanie Krill is identified as her
Wikimedia contact, but I don't know if Melanie is on this list: I'm
assuming not.
Thanks,
Sue
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---------- Forwarded message ----------
From: McKee, Heidi A. <mckeeha(a)muohio.edu>
Date: 5 July 2012 11:33
Subject: Women and Wikipedia: Research Project and Proposal
To: "sue(a)wikimedia.org" <sue(a)wikimedia.org>
Dear Sue,
I am working with a doctoral student, Leigh Gruwell, who is studying
online writing, specifically looking at the ways in which
collaborative online spaces are experienced by women and could be made
even more conducive for female contributors. She is a supporter and
advocate of Wikipedia, and she has found your work and your goal to
increase female participation in Wikipedia inspiring.
Back in March Leigh submitted a proposal to Wikipedia which was early
this summer reviewed by Melanie Kill. The proposal now awaits final
review/approval from Wikipedia. Here is her proposal:
http://meta.wikimedia.org/wiki/Research:Women_and_Wikipedia:_Contributions_…
She had hoped to interview female contributors this spring and now
hopes to do so this summer, but without hearing from Wikipedia she's
unable to conduct her research. Leigh is committed to sharing all of
her research data with Wikipedia and she has IRB-approval from Miami
University (pending approval from Wikipedia).
Thank you,
Heidi
********
Heidi McKee
Associate Professor
Director, Professional Writing
English Department
Miami University
Bachelor 364
Oxford, Ohio 45056
513-529-2635 (office)
mckeeha(a)muohio.edu
I thought this was interesting so I’m passing it along. This sentence particularly caught my attention: “The answer, I think, is to take the best of what both experts and markets have to offer, realizing that the combination of the two offers a better window onto the future than either alone.” Substitute the word “crowds” for “markets”, and perhaps there is something here that could be applied to Wikipedia in our quest for quality, mixing the best of expertise and crowdsourcing. I’d be very interested in hearing comments from other Wikipedians.
https://www.nytimes.com/2012/07/08/sunday-review/when-the-crowd-isnt-wise.h…
Cheers,
Pine
For those of you who are interested in reverts:
I just presented our paper on accurate revert detection at the ACM Hypertext and Social Media conference 2012, showing a significant accuracy (and coverage) gain compared to the widely used method of finding identical revisions (via MD5 hash values) to detect reverts, proving that our method detects edit pairs that are significantly more likely to be actual reverts according to editors perception of a revert and the Wikipedia definition. 35% of the reverts found by the MD5 method in our sample are not assessed to be reverts by more than 80% of our survey participants (accuracy 0%). The provided new method finds different reverts for these 35% plus 12% more, which show a 70% accuracy.
Find the PDF slides, paper and results here:
http://people.aifb.kit.edu/ffl/reverts/
I'll be happy to answer any questions.
More in detail:
The MD5 hash method employed by many researchers to identify reverts (as some others, like using edit comments) is acknowledged to produce some inaccuracies as far as the Wikipedia definition of a revert ("reverses the actions of any editors", "undoing the actions"..) is concerned. The extent of these inaccuracies is usually judged to be not too large, as naturally, most reverting edits are carried out immediately after the edit to be reverted, being an "identity revert" (Wikipedia definition: "..normally results in the page being restored to a version that existed previously"). Still, there has not been a user evaluation assessing how well the detected reverts conform with the Wikipedia definition and what users actually perceive as a revert. We developed and evaluated an alternative method to the MD5 identity revert and show a significant increase in accuracy (and coverage).
34% of the reverts detected by the MD5 hash method in our sample actually fail to be acknowledged as full reverts by more than 80% of users in our study, while our new method performs much better, finding different reverts for these 34% wrongly detected reverts plus 12% more reverts, showing an accuracy of 70% for these newly found edit pairs actually being reverts according to the users. The increased accuracy performance between the reverts detected only by the MD5 and only by our new method is highly significant, while reverts detected by both methods also perform significantly better than those only detected by the MD5 method.
Trade-off:
Although this method is much slower than the MD5 method (as it is using DIFFs between revisions) it reflects much better what users (and the Wikipedia community as a whole) see as a revert. It thereby is a valid alternative if you are interested in the antagonistic relationships between users on a more detailed and accurate level. There is quite some potential to make it even faster by combining the two methods, decreasing the number of DIFFs to be performed, let's see if we can come around doing that :)
The scripts and results listed in the paper can be found at http://people.aifb.kit.edu/ffl/reverts/
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
Skype: f.floeck_work
E-Mail: fabian.floeck(a)kit.edu<mailto: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
Hello,
under http://dumps.wikimedia.org/other/articlefeedback/ are some
valuable dumps of feedback on articles dating 2011. The feedback data
does not seem to be part of the regular dumps, maybe because it is only
available in few installations/languages.
Does anyone know of a source for the feedback data which is continously
updated?
Thank you in advance for any hints.
Best wishes,
Rüdiger
--
Rüdiger Gleim
Goethe Universität
Senckenberganlage 31
60325 Frankfurt
Hi all,
Guillermo Garrido (NLP Group, UNED, Spain) and Enrique Alfonseca Google
Research Zurich, one of our partners in the RENDER project [1] extracted a
data set that contains all attribute-value pairs of info boxes out of
English Wikipedia articles since 2003.
This 5.5 GB large data set, which is called Wikipedia Historical Attributes
Data (WHAD), is freely available on the download page of the RENDER toolkit
[2].
More detailed information about the data set can be found at Enrique
Alfonseca's website [3].
Enrique will attend the Wikipedia Academy 2012 [4] and is going to present
his work during the Paper Session III: Analyzing Wikipedia Article Data [5]
on Saturday.
A short preview of this paper was published in the current
Research:Newsletter [6].
Best regards from Berlin,
Angelika
[1] http://meta.wikimedia.org/wiki/RENDER
[2] http://toolserver.org/~RENDER/toolkit/downloads/
[3] http://alfonseca.org/eng/research/whad.html
[4] http://wikipedia-academy.de/
[5]
http://wikipedia-academy.de/2012/wiki/Schedule#Paper_Session_III:_Analysing…
[6] https://meta.wikimedia.org/wiki/Research:Newsletter/2012-06-25
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