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I tweeted out another link to the post around 2pm, and then at 2:30pm, it moved a bit further up the results on the 3rd page:

The Latest results factors in real-time linking behavior, so it is picking up all the tweets where my post was being RTed, and as a result, the top half of the Latest results for the term “Social Media Crisis Management” were completely devoted to MY post.

That’s a perfect example of how Twitter and Facebook sharing is now impacting Google results.  And it’s also a wonderful illustration of the value of being active on Twitter.  I tweeted a link to that post several times yesterday and this morning, which was a big reason why it moved up the Google results so quickly, and a big reason why it dominated the Latest results for that term.

there are two things I want you to take away from this: 1 – This was very basic SEO stuff that any of you can do.  It was simply a case of targeting a specific phrase, and inserting it in the post.  Now as far as my having a large and engaged Twitter network and readership here (thanks guys!), that definitely played a big factor in the post moving up the results so quickly.  But at a basic level, everything I did from a SEO perspective is what you can do with every post.  And you should.

2 – You can best learn by breaking stuff.  There are a gazillion ‘How to’ and ’10 Steps to…’ articles about using social media, and I have certainly written my fair share of these.  But the best way *I* learn is if you can show me the first 1 or 2 steps, then leave me alone and let me figure out the remaining 8 or 9 steps for myself.  Don’t just blindly follow my social media advice or anyone else’s.  Use the advice as a guide for how you can get started.  But there is no one RIGHT way to use social media.  Never forget that.  I can tell you what works for me and my clients, but you still need to tweak any advice so that it is perfect for you.  SEO geeks will no doubt see a ton of things that I could have done or altered in this experiment to get even better results.  And moving forward, I am going to continue to tweak and ‘break stuff’ in order to better figure out how all the moving parts work together.

So who produces all this bulk jive? Business Insider, the business-news site, has provided a forum to a half dozen low-paid content farmers, especially several who work at AOL’s enormous Seed and Patch ventures. They describe exhausting and sometimes exploitative writing conditions. Oliver Miller, a journalist with an MFA in fiction from Sarah Lawrence who once believed he’d write the Great American Novel, told me AOL paid him about $28,000 for writing 300,000 words about television, all based on fragments of shows he’d never seen, filed in half-hour intervals, on a graveyard shift that ran from 11 p.m. to 7 or 8 in the morning.

Mr. Miller’s job, as he made clear in an article last week in The Faster Times, an online newspaper, was to cram together words that someone’s research had suggested might be in demand on Google, position these strings as titles and headlines, embellish them with other inoffensive words and make the whole confection vaguely resemble an article. AOL would put “Rick Fox mustache” in a headline, betting that some number of people would put “Rick Fox mustache” into Google, and retrieve Mr. Miller’s article. Readers coming to AOL, expecting information, might discover a subliterate wasteland. But before bouncing out, they might watch a video clip with ads on it. Their visits would also register as page views, which AOL could then sell to advertisers.

commodify writing: you pay little or nothing to writers, and make readers pay a lot — in the form of their “eyeballs.” But readers get zero back, no useful content.

You can’t mess with Google forever. In February, the corporation concocted what it concocts best: an algorithm. The algorithm, called Panda, affects some 12 percent of searches, and it has — slowly and imperfectly — been improving things. Just a short time ago, the Web seemed ungovernable; bad content was driving out good. But Google asserted itself, and credit is due: Panda represents good cyber-governance. It has allowed Google to send untrustworthy, repetitive and unsatisfying content to the back of the class. No more A’s for cheaters.

the goal, according to Amit Singhal and Matt Cutts, who worked on Panda, is to “provide better rankings for high-quality sites — sites with original content and information such as research, in-depth reports, thoughtful analysis and so on.”

Google officially rolled out Panda 2.2. Put “Whitey Bulger” into Google, and where you might once have found dozens of content farms, today you get links to useful articles from sites ranging from The Boston Globe, The Los Angeles Times, the F.B.I. and even Mashable, doing original analysis of how federal agents used social media to find Bulger. Last month, Demand Media, once the most notorious of the content farms, announced plans to improve quality by publishing more feature articles by hired writers, and fewer by “users” — code for unpaid freelancers. Amazing. Demand Media is stepping up its game.

it's in these companies' hands to do this ethically--to build algorithms that show us what we need to know and what we don't know, not just what we like.

why would the Googles and Facebooks of the world change what they're doing (absent government regulation)? My hope is that, like newspapers, they'll move from a pure profit-making posture to one that recognizes that they're keepers of the public trust.

most people don't know how Google and Facebook are controlling their information flows. And once they do, most people I've met want to have more control and transparency than these companies currently offer. So it's a way in to that conversation. First people have to know how the Internet is being edited for them.

what's good and bad about the personalization. Tell me some ways that this is not a good thing? Here's a few. 1) It's a distorted view of the world. Hearing your own views and ideas reflected back is comfortable, but it can lead to really bad decisions--you need to see the whole picture to make good decisions; 2) It can limit creativity and innovation, which often come about when two relatively unrelated concepts or ideas are juxtaposed; and 3) It's not great for democracy, because democracy requires a common sense of the big problems that face us and an ability to put ourselves in other peoples' shoes.

Stanford researchers Dean Eckles and Maurits Kapstein, who figured out that not only do people have personal tastes, they have personal "persuasion profiles". So I might respond more to appeals to authority (Barack Obama says buy this book), and you might respond more to scarcity ("only 2 left!"). In theory, if a site like Amazon could identify your persuasion profile, it could sell it to other sites--so that everywhere you go, people are using your psychological weak spots to get you to do stuff. I also really enjoyed talking to the guys behind OKCupid, who take the logic of Google and apply it to dating.

Nobody noticed when Google went all-in on personalization, because the filtering is very hard to see.

We could continuously sample a news source’s output to produce ongoing accuracy estimates, and build social software to help the audience report and filter errors. Meticulously applied, this approach would give a measure of the accuracy of each information source, and a measure of the efficiency of their corrections process (currently only about 3% of all errors are corrected.)

Real world reporting isn’t always clearly “right” or “wrong,” so it will often be hard to decide whether something is an error or not. But we’re not going for ultimate Truth here,  just a general way of measuring some important aspect of the idea we call “accuracy.” In practice it’s important that the error counting method is simple, clear and repeatable, so that you can compare error rates of different times and sources.

Subjective errors, though by definition involving judgment, should not be dismissed as merely differences in opinion. Sources found such errors to be about as common as factual errors and often more egregious [as rated by the sources.] But subjective errors are a very complex category

One of the major problems with previous news accuracy metrics is the effort and time required to produce them. In short, existing accuracy measurement methods are expensive and slow. I’ve been wondering if we can do better, and a simple idea comes to mind: sampling. The core idea is this: news sources could take an ongoing random sample of their output and check it for accuracy — a fact check spot check

Standard statistical theory tells us what the error on that estimate will be for any given number of samples (If I’ve got this right, the relevant formula is standard error of a population proportion estimate without replacement.) At a sample rate of a few stories per day, daily estimates of error rate won’t be worth much. But weekly and monthly aggregates will start to produce useful accuracy estimates

the first step would be admitting how inaccurate journalism has historically been. Then we have to come up with standardized accuracy evaluation procedures, in pursuit of metrics that capture enough of what we mean by “true” to be worth optimizing. Meanwhile, we can ramp up the efficiency of our online corrections processes until we find as many useful, legitimate errors as possible with as little staff time as possible. It might also be possible do data mining on types of errors and types of stories to figure out if there are patterns in how an organization fails to get facts right.

I’d love to live in a world where I could compare the accuracy of information sources, where errors got found and fixed with crowd-sourced ease, and where news organizations weren’t shy about telling me what they did and did not know. Basic factual accuracy is far from the only measure of good journalism, but perhaps it’s an improvement over the current sad state of affairs