Hi Fabian,
That looks interesting, but I wondered if you were aware of some of the
possible results when you are editing Wikipedia articles section by section?
If an article has multiple sections then it doesn't matter how many edits
have been made to other sections, if you want to undo the most recent edit
to a particular section then you can just hit undo or rollback and revert
it. The contents of the whole article will be a new and potentially unique
revision as one section will have reverted to what it was before it was
vandalised and the other sections will be as they were before the latest
revert.
You could get some interesting examples by looking at the history of the
article on Sarah Palin on the night she became John McCain's running mate.
The edit rate peaked at 25 edits per minute, that should make it a good
example of an article where edits were only being done one section at a
time as anyone who tried to edit the whole article would have been pretty
much guaranteed an edit conflict. As I remember it there were multiple edit
wars taking place simultaneously in different sections of the article, none
would have taken the whole article back to a previous version, just one
section.
WereSpielChequers
On 27 June 2012 18:05, Floeck, Fabian (AIFB) <fabian.floeck(a)kit.edu> wrote:
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
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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