I'm waiting for someone to develop a wikipedia client for OS X that uses core animation (and other frameworks) to animate changes in an article over time. There's a huge amount of potential on the client side because Wikipedia, server side, is always concentrating on how to make sure the doesn't go down; there's not enough money for frills.
On 7/30/07, [EMAIL PROTECTED] <[EMAIL PROTECTED]> wrote: > I really like this project. It's hard to understand the history of a text. > It reminds me of manuscript analysis, like looking at palimpsets, etc. > > However, listening to your goals, it sounds like a difficult task. It will > be very hard to stabilize your model of trustworthiness, since it is based > on a some assumptions (e.g. what does someone read when they edit a page?) > that are hard to nail down. > > As you are tuning your algorithms, you might take another approach to > simplify matters. It's easier and could be more useful to visualize > behaviour without trying to draw conclusions about what that behaviour > might indicate. This is more powerful in many ways since your algorithm > will never possess knowledge of the full social context that a given user > will have. i.e. maybe a trusted user has gotten into a heated dispute and > become erratic, and no longer trustworthy? > > A good summary of how to do social visualizations well is Erickson, 2003 > (cf. http://www.bibwiki.com/wiki/design?Erickson,+2003) > > These practices are for building a tool that can be used amongst the > entire social group. If you're after a particular research question (i.e. > how influential are trusted authors?), they don't apply as well. > > Cheers, > Sunir > > > Dear Andre, > > > > let me say that the algorithms need tuning, so we are not sure we are > > doing > > the best, but here is the idea: > > > > When a user of reputation 10 (for example) edits the page, the text that > > is > > added only gets trust 6 or so. It is not immediately considered high > > trust, > > because others have not yet had a chance to vet it. > > > > When a user of reputation 10 edits the page, the trust of the text already > > on the page raises a bit (over several edits, it would approach 10). This > > models the fact that the user, by leaving the text there, gave an implicit > > vote of assent. > > > > The combination of the two effects explains what you are seeing. > > The goal is that even high-reputation authors can only lend part of their > > reputation to the text they create; community vetting is still needed to > > achieve high trust. > > > > Now as I say, we must still tune the various coefficients in the > > algorithms > > via a learning approach, and there is a bit more in the algorithm than i > > describe above, but that's the rough idea. > > > > Another thing I am pondering is how much a reputation change should spill > > over paragraph or bullet-point breaks. I could change easily what I do, > > but > > I will first set up the optimization/learning - I want to have some > > quantitative measure of how well the trust algo behaves. > > > > Thanks for your careful analysis of the results! > > > > Luca > > > > On 7/30/07, Andre Engels <[EMAIL PROTECTED]> wrote: > >> > >> 2007/7/29, Luca de Alfaro <[EMAIL PROTECTED]>: > >> > >> > We first analyze the whole English Wikipedia, computing the reputation > >> of > >> > each author at every point in time, so that we can answer questions > >> like > >> > "what was the reputation of author with id 453 at 5:32 pm of March 14, > >> > 2006". The reputation is computed according to the idea of > >> content-driven > >> > reputation. > >> > > >> > For new portions of text, the trust is equal to (a scaling function > >> of) > >> the > >> > reputation of the text author. > >> > Portions of text that were already present in the previous revision > >> can > >> gain > >> > reputation when the page is revised by higher-reputation authors, > >> especially > >> > if those authors perform an edit in proximity of the portion of text. > >> > Portions of text can also lose trust, if low-reputation authors edit > >> in > >> > their proximity. > >> > All the algorithms are still very preliminary, and I must still apply > >> a > >> > rigorous learning approach to optimize the computation. > >> > Please see the demo page for more details. > >> > >> One thing I find peculiar is that adding a text somewhere can lower > >> the trust of the surrounding text while at the same thing heightening > >> that of far away text. Why is that? See for example > >> > >> http://enwiki-trust.cse.ucsc.edu/index.php?title=Collation&diff=prev&oldid=102784135 > >> - trust:6 text is added between trust:8 text, causing the surrounding > >> text to go down to trust:6 or even trust:5, but at the same time > >> improving text elsewhere in the page from trust:8 to trust:9. Why > >> would the author count as low-reputation for the direct environment, > >> but high-reputation farther away? > >> > >> -- > >> Andre Engels, [EMAIL PROTECTED] > >> ICQ: 6260644 -- Skype: a_engels > >> > > _______________________________________________ > > Wiki-research-l mailing list > > [email protected] > > http://lists.wikimedia.org/mailman/listinfo/wiki-research-l > > > > > > _______________________________________________ > Wiki-research-l mailing list > [email protected] > http://lists.wikimedia.org/mailman/listinfo/wiki-research-l > -- Ben Yates Wikipedia blog - http://wikip.blogspot.com _______________________________________________ Wiki-research-l mailing list [email protected] http://lists.wikimedia.org/mailman/listinfo/wiki-research-l
