Re: Wikipedia colored according to trust
> You are evaluating the coloring against a performance criterion that is not the one we designed it for.
>
> Our coloring gives orange color to new information that has been added by low-reputation authors. New
information by
> high-reputation authors is light orange. As the information is revised, it gains trust.
>
> Thus, our coloring answers the question, intuitively: has this information been revised already?
Have reputable
> authors looked at it?
>
> You are asking the question: how much information colored orange is questionable?
> This is a different question, and we will never be able to do well, for the simple reason that it is well known
that
> a lot of the correct factual information on Wikipedia comes from occasional contributors, including
anonymous
> authors, and those occasional contributors and anonymous will have low reputation in most conceivable
reputation
> systems.
Before I go further, let me reiterate that I think your work is excellent and has the potential for adding
huge value to the wiki world. If I didn't think so, I wouldn't bother writing this message.
I think it's important to evaluate a system like this in terms of a metric that captures some sort of value
added to some category of wiki end user.
The system you are trying to build could provide HUGE value for the end user, if it could allow him to tell with
a certain amount of certainty (say, > 60%) which parts of the system are questionable and which parts are
not. This is the metric I used in my admittedly very small test (Note: I'm sure it's not the only metric that
could be used to measure end-user value).
Based on that very preliminary test, it seems your system does not do a great job at that, and you seem to say
that you don't think it could.
That's OK. I'm sure there is SOMETHING that this system can do for the end user, because he "internal"
performance metrics you list in your message seem to indicate that there is some substance to the
predictions of the algorithm.
> We do not plan to do any large-scale human study. For one, we don't have the resources.
A study with human judges does not have to be large scale. I would guess 30 subjects would do the trick.
> For another, in the very
> limited tests we did, the notion of "questionable" was so subjective that our data contained a HUGE amount
of
> noise. We asked to rank edits as -1 (bad), 0 (neutral), +1 (good). The probability that two of us agreed
was
> somewhere below 60%. We decided this was not a good way to go.
That's interesting. I would have expected a large amount of agreement based on my assumption that the
majority of edits are either clearly Good or Neutral. In other words, I would have expected judges to
disagree only on the "iffy" portion of the edits, but since I assume that this is a small portion of all
edits, you would still have large agreement. I guess my assumptions are wrong.
Is the story the same if you look at only two categories: Reject (= your {-1} set) and Keep (your {0, +1} set)?
> The results of our data-driven evaluation on a random sample of 1000 articles with at least 200 revisions
each showed
> that (quoting from our paper):
> * Recall of deletions. We consider the recall of low-trust as a predictor for deletions. We show that text
in the
> lowest 50% of trust values constitutes only 3.4% of the text of articles, yet corresponds to 66% of the
text that is
> deleted from one revision to the next.
> * Precision of deletions. We consider the precision of low-trust as a predictor for deletions. We show
that text
> that is in the bottom half of trust values has a probability of 33% of being deleted in the very next
revision,
> in contrast with the 1.9% probability for general text. The deletion probability raises to 62% for
text in the
> bottom 20% of trust values.
> * Trust of average vs. deleted text. We consider the trust distribution of all text, compared to the trust
> distribution to the text that is deleted. We show that 90% of the text overall had trust at least 76%,
while the
> average trust for deleted text was 33%.
> * Trust as a predictor of lifespan. We select words uniformly at random, and we consider the statistical
correlation
> between the trust of the word at the moment of sampling, and the future lifespan of the word. We show that
words
> with the highest trust have an expected future lifespan that is 4.5 times longer than words with no
trust. We remark
> that this is a proper test, since the trust at the time of sampling depends only on the history of the word
prior to
> sampling.
Those measures tell me that there is definitely something to the algorithm, and I am trying to help you
define what value it could provide to the which kind of end user.
One concern I have though. Have you compared your system to a naïve implementation which simply uses the
edit's "age" as a measure of its trustworthiness? In other words, don't worry about who created the edit or
modified it. Just worry about how long it's been there.
Alain
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