Posted by ttyhfbsdfg on June 2nd, 2008
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Judging Credibility
|
Jeff Jarvis points
out the flaws in Newscred.
It’s very simple — though that’s the problem; credibility isn’t so
simple. They list articles and you get to “credit” or “discredit”
them. These scores are, in turn, compiled for writers and
publications.
The first and most obvious problem, which TechCrunch points out, is
that this is bait for grudges. Fox from one side, the Times from the
other will get discredited by their detractors all day long. One
man’s bias is often the other man’s truth.
Precisely: reputation, credibility, are personal judgments. We often
think of the New York Times having a reputation, but in fact,
there’s not some single, magic score somewhere. There are millions
of opinions. But there are also millions of reputations. Each
person judges what the reputation of the NYT is based on what they
think others are thinking. Any system that doesn’t allow
each person to have a personalized reputation for a given entity is
doomed to suffer this problem.
But Jeff points out a second problem:
The second and more fundamental problem is that there’s no basis to
decide credibility. Does one error ruin an article’s credibility? How
many discredits does it take to ruin a reporter’s or a publication’s?
And then what does that mean? That they lied? That you don’t believe
them? That you don’t like them? That they make mistakes? That they
don’t report enough? That they use anonymous sources? That they
relied on bad sources? That they wrote it badly? That they weren’t
transparent?
Just as each person keeps an individual idea of the reputation of
every other entity they care about, they also use their own basis for
making that judgment. Apes are remarkably good at this–we have to be
to create large social structures. Our frustration with
computational systems that attempt to do this for us is partly rooted
in the fact that they don’t come close to the nuanced sophisticated
social judgments that any 6 year old can make.
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Judging Credibility
|
Jeff Jarvis points
out the flaws in Newscred.
It’s very simple — though that’s the problem; credibility isn’t so
simple. They list articles and you get to “credit” or “discredit”
them. These scores are, in turn, compiled for writers and
publications.
The first and most obvious problem, which TechCrunch points out, is
that this is bait for grudges. Fox from one side, the Times from the
other will get discredited by their detractors all day long. One
man’s bias is often the other man’s truth.
Precisely: reputation, credibility, are personal judgments. We often
think of the New York Times having a reputation, but in fact,
there’s not some single, magic score somewhere. There are millions
of opinions. But there are also millions of reputations. Each
person judges what the reputation of the NYT is based on what they
think others are thinking. Any system that doesn’t allow
each person to have a personalized reputation for a given entity is
doomed to suffer this problem.
But Jeff points out a second problem:
The second and more fundamental problem is that there’s no basis to
decide credibility. Does one error ruin an article’s credibility? How
many discredits does it take to ruin a reporter’s or a publication’s?
And then what does that mean? That they lied? That you don’t believe
them? That you don’t like them? That they make mistakes? That they
don’t report enough? That they use anonymous sources? That they
relied on bad sources? That they wrote it badly? That they weren’t
transparent?
Just as each person keeps an individual idea of the reputation of
every other entity they care about, they also use their own basis for
making that judgment. Apes are remarkably good at this–we have to be
to create large social structures. Our frustration with
computational systems that attempt to do this for us is partly rooted
in the fact that they don’t come close to the nuanced sophisticated
social judgments that any 6 year old can make.
Tags:
reputation
|
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