Good point.

 

I did not look for false negatives because of lack of time. That would have 
required me to read the whole content of the pages, and look for items that I 
thought were questionable eventhough they weren't highlighted by the system.

 

I agree with you that false negatives may be just as important as false 
positives. It's just more work to evaluate that metric. 

 

Alain

 

 

From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Ward Cunningham
Sent: December 20, 2007 11:01 AM
To: Research into Wikimedia content and communities
Subject: Re: [Wiki-research-l] Wikipedia colored according to trust

 

Alain -- Is it true that although you've seen 3x to 15x false positive, that 
you did not see any false negatives? By false negative I would mean a 
questionable item that was not highlighted. Maybe you weren't looking for 
these? Best regards. -- Ward


__________________

Ward Cunningham

503-432-5682

 

 





 

On Dec 20, 2007, at 6:12 AM, Desilets, Alain wrote:





Here is my feedback based on looking at a few pages on topics that I know very 
well.

 

Agile Software Development

·        http://wiki-trust.cse.ucsc.edu/index.php/Agile_software_development

·        Not bad. I counted 13 highlighted items, 5 of which I would say are 
questionable.

 

Usability

·        http://wiki-trust.cse.ucsc.edu/index.php/Usability

·        Not as good. 14 highlighted items 3 of which I would say are 
questionable.

 

Open Source Software

·        http://wiki-trust.cse.ucsc.edu/index.php/Open_source_software

·        Not so good either. 23 highlighted items, 3 of which I would say are 
questionable.

 

This is a very small sample, but it's all I have time to do. It will be 
interesting to see how other people rate the precision of the highlightings on 
a wider set of topics. Based on these three examples, it's not entirely clear 
to me that this system would help me identify questionable items in topics that 
I am not so familiar with.

 

Are you planning to do a larger scale evaluation with human judges? An issue in 
that kind of study is to avoid favourable or disfavourable bias on the part of 
the judges. Also, you have to make sure that your algorithm is doing better 
than random guessing (in other words, there may be so many questionable phrases 
in a wiki page that random guessing would be bound to guess right ounce out of 
every say, 5 times). One way to avoid these issues would be to produce pages 
where half of the highlightings are produced by your system, and the other half 
are highlighting a randomly selected contiguous contribution by a single author.

 

I think this is really interesting work worth doing, btw. I just don't know how 
useful it is in its current state.

 

Cheers,

 

Alain Désilets

 

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