Case Studies - Information Trapping: Real-Time Research on the Web - page 237

Bingo! He's halved his results, the irst ten results were generated over a couple
of weeks, and a cursory glance at the page summaries shows that these stories
are exactly the sort of thing he's looking for. So he saves that search. Ater some
more experimenting, he also settles on the phrase “outsource to Taiwan” , as
well as the queries “DRAM production” Taiwan , and DRAM outsourcing .
Fred has generated two searches that address speciically what his boss has
asked him about. But beyond that he has also generated a search that will
give him ongoing information about the whole issue of outsourcing to Tai-
wan in the irst place, as well as a couple of searches that remove Taiwan
from the equation and look at the whole issue of computer manufacturing
and outsourcing in general.
By monitoring these information traps, Fred will be able to both directly
answer his boss's question and perhaps make a few suggestions of his own!
(Maybe Fred will discover that Taiwan is not the best place to outsource
some of its manufacturing—maybe it's India, China, someplace else, or
nowhere at all.)
With these queries, Fred begins building his traps by starting at a news
search engine.
Step 2: Using news search engines
Fred ponders how urgent his need is to get this information. He inally
decides that he will have the Google News updates delivered to his cell
phone, just to give him constant feedback about Taiwan and outsourcing
in general. he rest of the searches he'll set up as RSS feeds and specify a
certain time every morning to go over what's been delivered to him.
He stays at Google News, reruns the searches, and takes advantage of the
option to send the results to his cell phone's e-mail address. hen he goes
to Kebberfegg (researchbuzz.org/tools/kebberfegg.com), chooses the News
option, and one by one generates OPML iles for his queries. He then imports
these iles into his newsreader of choice, which happens to be NewsGator, and
deletes the Google News feeds since he's already getting them by e-mail.
Fred now has the cornerstone of his information trapping—fresh informa-
tion being delivered to him from two or three diferent angles. He will use the
information that he gains from these searches to tweak his queries over time.
Perhaps a new company pops up in Taiwan that does what his company is