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September 01, 2008

Words Matter

"Gustav Sends Oil Prices Surging" read the headline on the front page of cnn.com yesterday.

"Crude rises slightly in 'muted response' during a special electronic trading session. Hurricane bears down on region that controls 25% of U.S. crude production," read the subhead of the story itself if you bothered to click through to it. It went on to say:

"This is a very, very muted response," said Peter Beutel, an oil analyst with Cameron Hanover. "The fact that it's trading this low shows how powerful the dollar is and how bearish this market has become; had this happened in early July, oil would have been up $10 now."

So the headline about surging oil prices is really a story about a weak (at least right now) oil market.

We've got important decisions to make. The Russian empire would like to be resurgent; we're damned near hostage to oil imported from unfriendly and/or unstable places; the globe is probably warming (but may start cooling) and we may have caused it and we may be able to do something about it and/or may have to live with the consequences. We need to elect a president for the next four years and decide whether Democrats should have a veto-proof and filibuster-resistant majority in Congress. This stuff matters.

So the words in headlines and news stories matter as well. Subtleties of wording get right to our emotions bypassing our logic as in the first two lines from a front page story in yesterday's NY Times:

"The White House has long touted the "surge" of forces in Iraq as one of President Bush's proudest achievements. But that decision, one of Mr. Bush's most consequential as commander in chief, was made only after months of tumultuous debate within the administration, according to still-secret memorandums and interviews with a broad range of current and former officials."

It's the "But" at the beginning of the sentence which should've caught the editor's eye. Why "But"? Is a decision a bad (or good) decision because it was proceeded by debate? Is the "White House" misleading people by claiming this as an achievement BECAUSE there was debate before it happened. Quite possibly unintentionally, writer Michael R. Gordon is betraying his bias against either the President or the surge with the insidious "But". BTW, the "touted" in the lead sentence is also a bit prejudicial. The rest of the story is interesting detail over how the debate took place (in an administration not known for debate) and who said what (without much attribution to named sources). The point isn't whether the surge is right or wrong; the point is that even the editors of a usually great newspaper don't recognize bias even in the lede for a front page story. We have to look out for ourselves and keep our debunkers on.

It's hard to blame reporters for bias when seemingly-qualified sources exhibit it as well. On August 26 an organization called National Snow and Ice Data Center (NSIDC), which is part of the Cooperative Institute for Research in Environmental Sciences at the University of Colorado at Boulder, put out a press release with the title "Arctic sea ice now second-lowest on record". This headline was duly picked up by various news organizations.

Now that might be alarming unless you read around the site and find that the record was set last year and that most of the experts they poll expected the Arctic ice to retreat even further, if only because it started from last summer's low extent. An article on June 11, 2008 on the NY Times' Dot Earth site said "Most Experts Foresee a Repeat, at Least, of 2007 Arctic Ice Loss".

So the headline on the NSIDC release would have been more informative if it said "Arctic Ice Cap Grows from Record Lows Contradicting Expert Predictions".

One year's observations hardly a trend make; that's not the point. In fact the whole record of these observations only go back to the beginning of the satellite era. What is important is that we are fed a surfeit of "information" highly laced with bias towards the conclusions the writers would like us to draw. We need to be diligent in sifting the information out of this mix before we feed it into our own decision-making algorithms.

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