Because the rules are quite rigid, e.g. newer is *always* better, different organizations try to have the newest stories about a given popular event. So, in the lead up to the early December snowstorm here in California, the Weather Channel's website published a great preview of the storm on November 29th or 30th. I read it on or about when it came out. *After* the storm on December 3rd, I went looking to see which of the predictions from the story had come true. I popped a few search terms into Google News and lo and behold, there was a December 3rd story from the Weather Channel. Excitedly, I clicked through the link and found ... the exact same preview with a timestamp that now read December 3, 2012, 9:08 AM.
Keep in mind that this now makes the story completely nonsensical. It is a preview of an event dated after that event has already passed. It's like a story dated November 7th story about who might win the presidential election. A Christmas preview on December 29th.
In short, this is lunacy! At least to a human.
But to a machine, this looks like a "fresh" story with lots of keywords about the Shasta snowfall. The machine can't tell that the article is written in the future tense or that it is worse than useless now. This type of thing actively degrades the news ecosystem, and it's only happening because of the way that Google's algorithm works.
Granted, this is the lawless variety of optimizing for Google News. But there are a lot of examples and techniques that have developed solely because of the way the algorithm works. If you really want to peer down the rabbit hole, take a look at the depth of the analysis in this series of posts on the "Top 10 Most Important Google News Ranking Factors." It was assembled by a team of people at some top publications, agencies, and SEO shops. Keep in mind that some of these optimizations benefit human beings. Punny headlines are slowly dying, and I'm OK with that. But other factors that Google is looking for -- like keyword density -- reward people who write the way that everyone else does, using the same words and using them frequently. Google also rewards specialists over generalists. If you (as author or site) publish a ton on one thing, you're more likely to move up the rankings than if you take a more horizontal view of a field (say, technology). And lastly, take a look at the Google News front page now. It's almost exclusively traditional media outlets. It's actually shocking how little at least I see from media entities created after 2004. There's a shocking apolitical conservatism to however Google's algorithm works.
My point in discussing these details at such length is to strengthen Diakopoulos' point about the lack of "objectivity" in algorithmic operations. Even if one could design some perfectly balanced system that had no observable bias when it began to run, the people who are producing the inputs to the system and dependent on the outputs will begin to adjust their behavior. They'll change to make themselves more legible to the machine, and those that do so best will prosper.