Demo: coloring the text of the Wikipedia according to its trust

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Luca de Alfaro

29 Jul 2007 29 Jul '07

6:05 a.m.

Dear All: I would like to tell you about a demo we set up, where we color the text of Wikipedia articles according to a computed value of trust. The demo is available at http://trust.cse.ucsc.edu/ The trust value of each word of each revision is computed according to the reputation of the original author of the text, as well as the reputation of all authors that subsequently revised the text. We have uploaded a few hundred pages; for each page, we display the most recent 50 revisions (we analyzed them all, but we just uploaded the most recent 50 to the server). Of course, there are many other uses of text trust (for example, one could have the option of viewing a "recent high-trust version" of each page upon request), but I believe that this coloring gives an intuitive idea of how it could work. I will talk about this at Wikimania, for those of you who will be there. I am looking forward to Wikimania! Details: 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 <http://www.soe.ucsc.edu/%7Eluca/papers/07/wikiwww2007.html>. 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. All the best, Luca de Alfaro http://www.soe.ucsc.edu/~luca

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Ben Yates

30 Jul 30 Jul

3:17 a.m.

Awesome! I posted a long response on my blog -- http://wikip.blogspot.com/2007/07/alright-this-is-most-awesome-thing-i.html On 7/29/07, Luca de Alfaro <luca(a)soe.ucsc.edu> wrote:

...

Dear All: I would like to tell you about a demo we set up, where we color the text of Wikipedia articles according to a computed value of trust. The demo is available at http://trust.cse.ucsc.edu/ The trust value of each word of each revision is computed according to the reputation of the original author of the text, as well as the reputation of all authors that subsequently revised the text. We have uploaded a few hundred pages; for each page, we display the most recent 50 revisions (we analyzed them all, but we just uploaded the most recent 50 to the server). Of course, there are many other uses of text trust (for example, one could have the option of viewing a "recent high-trust version" of each page upon request), but I believe that this coloring gives an intuitive idea of how it could work. I will talk about this at Wikimania, for those of you who will be there. I am looking forward to Wikimania! Details: 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. All the best, Luca de Alfaro http://www.soe.ucsc.edu/~luca _______________________________________________ Wiki-research-l mailing list Wiki-research-l(a)lists.wikimedia.org http://lists.wikimedia.org/mailman/listinfo/wiki-research-l

-- Ben Yates Wikipedia blog - http://wikip.blogspot.com

Andre Engels

8:01 a.m.

2007/7/29, Luca de Alfaro <luca(a)soe.ucsc.edu>du>:

...

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&am… - 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, andreengels(a)gmail.com ICQ: 6260644 -- Skype: a_engels

Luca de Alfaro

8:10 a.m.

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 <andreengels(a)gmail.com> wrote:

...

2007/7/29, Luca de Alfaro <luca(a)soe.ucsc.edu>du>:

We first analyze the whole English Wikipedia, computing the reputation

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.

sunir＠sunir.org

3:23 p.m.

New subject: Demo: coloring the text of the Wikipedia according to its trust

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

...

2007/7/29, Luca de Alfaro <luca(a)soe.ucsc.edu>du>:

We first analyze the whole English Wikipedia, computing the reputation

each author at every point in time, so that we can answer questions

"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

their proximity. All the algorithms are still very preliminary, and I must still apply

rigorous learning approach to optimize the computation. Please see the demo page for more details.

_______________________________________________ Wiki-research-l mailing list Wiki-research-l(a)lists.wikimedia.org http://lists.wikimedia.org/mailman/listinfo/wiki-research-l

Ben Yates

1 Aug 1 Aug

7:49 a.m.

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, sunir(a)sunir.org <sunir(a)sunir.org> wrote:

...

2007/7/29, Luca de Alfaro <luca(a)soe.ucsc.edu>du>:

We first analyze the whole English Wikipedia, computing the reputation

each author at every point in time, so that we can answer questions

"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

their proximity. All the algorithms are still very preliminary, and I must still apply

rigorous learning approach to optimize the computation. Please see the demo page for more details.

_______________________________________________ Wiki-research-l mailing list Wiki-research-l(a)lists.wikimedia.org http://lists.wikimedia.org/mailman/listinfo/wiki-research-l

-- Ben Yates Wikipedia blog - http://wikip.blogspot.com

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Andre Engels
Ben Yates
Luca de Alfaro
sunir＠sunir.org