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
