Thanks for the comments! Yes, we very much wanted a system that
- does not change the day-to-day Wikipedia experience (it worked so
well so far, let's not change what worked; would people be put off, or
strange behaviors encouraged, by user-to-user ratings?),
- encourages lasting content, but does not punish people whose content
is rewritten/improved: people are mainly punished for reversions or partial
- does not display reputation associated with authors (newbies to the
Wikipedia provide a good share of the factual content, as they include many
domain experts, so it's important not to put them off)
- but still gives useful information to visitors on the trust of text
(and lots more can be done, e.g., getting on request the last high
trust version, ...)
As you point out, getting text diff to work is not trivial, and it took us a
long time to get something we liked; we had to write it from scratch... the
idea is given in the WWW07 paper: a greedy algorithm, that matches longest
substrings first, giving however a bias in favor of substrings that occur in
the same relative position in the pages. Moreover, we keep track not only
of the text present in a page (the "live" text), but also of the text that
used to be present, but has been deleted (the "dead" text). If you don't
this, reverts (and partial reverts) are not dealt with correctly.
We think that even better can be done, in fact (everything can always be
improved), but we haven't had a chance yet.
On 7/30/07, Daniel Mayer <maveric149(a)yahoo.com> wrote:
--- Erik Moeller <erik(a)wikimedia.org> wrote:
The University of Santa Cruz/California has an
interesting demo up
that computes author trust based on whether users' edits are
kept/improved or reverted. It then highlights passages of the text
according to the computed reputation of the author who added them:
NB, this is still very experimental, but it seems promising.
Luca de Alfaro, who did most of this work, will also be presenting at
Wow - that is even better than the idea of user-driven trust metrics. On
top of that, it
encourages activity we want to encourage; creating content that lasts and
is built on. It also
looks like they might have a better diff algorithm than we do.
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