One happy language!

[Jonathan Lighter]

Well, I suppose a genuine "positivity bias" in English would mean great
publicity for our beloved language and a possible open-sesame for grant
money to compare it with, say, Mandarin.

The authors - all five of them - seem unable to conceive of the distinction
between a happiness bias in the structure of a language (perhaps a la
Sapir-Whorf) and an overall cultural preference to accentuate the positive
whenever possible.

As Chris and Victor observe, however, the assignment of subjective
hedonometric values to individual words is, er, fraught with difficulty.
Participants "rated their happiness in response to each isolation." The
researchers "chose words solely on frequency of use."  So, on a scale of 1
to 9, which word makes you happier: "Of" or "at"?  "A" or "the"?  "I" or
"its"?
"Beer" or "sleep"?  "Hurricane" or "eruption"?  If I asked you a week from
now, would your ratings be the same?

I see that the authors have also published on climatological and "social and
biological contagion models." I'm sure there's no need for concern,
however.

JL

On Wed, Aug 31, 2011 at 5:53 PM, victor steinbok <aardvark66 at gmail.com>wrote:

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> Sender:       American Dialect Society <ADS-L at LISTSERV.UGA.EDU>
> Poster:       victor steinbok <aardvark66 at GMAIL.COM>
> Subject:      Re: One happy language!
>
> -------------------------------------------------------------------------------
>
> Wait! You mean 1 billion words on NYT and 360 billion words from GB boil
> down to 10000 unique words? How is that possible? It certainly sounds like
> there might have been some selection bias--it was just better hidden than
> merely picking up 10000 words from questionable sources. But, more to the
> point, the result of the study reflects the scale bias of the
> researchers--there is absolutely no indication of objectivity (nor is any
> possible) in ranking the words. This is simply a classic error that creeps
> up in most social sciences--attaching a random scale to non-quantifiable
> data will get you a neat numerical result, but will be totally devoid of
> actual meaning. Another recent classic in the same genre is UCLA Prof Tim
> Groseclose's book Left Turn: How Liberal Media Bias Distorts the American
> Mind (with Jeff Milyo), which Groseclose is peddling the last couple of
> days
> as a guest blogger on Volokh Conspiracy. Geoff Nunberg took the book apart
> on Language Log.
>
> http://goo.gl/AOjOc
>
> > But sand sifted statistically is still sand. If you take the trouble to
> > read the study carefully, it turns out to be based on unsupported,
> > ideology-driven premises and to raise what would it would be most polite
> to
> > describe as severe issues of data quality, however earnestly Groseclose
> and
> > Milyo crunched their numbers.
>
>
> The simple principle here is GIGO--no matter how nicely the numbers are
> tabulated.
>
> VS-)
>
>
> On Wed, Aug 31, 2011 at 4:21 PM, Ben Zimmer
> <bgzimmer at babel.ling.upenn.edu>wrote:
>
> >
> > Not to dampen your skepticism, Jon, but that's 10,000 *unique* words
> > (types, not
> > tokens). If you look at the study, you'll see they analyzed 9 billion
> words
> > from
> > Twitter, 360 billion words from Google Books, 1 billion words from The
> New
> > York
> > Times, and 59 million words from song lyrics. Presumably enough data to
> > overcome stylistic biases in the source material.
> >
> > --bgz
>
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>



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