I’m surprised that neither Stephen Wolfram nor Nick Felton haven’t yet tackled the “change in my pocket” analysis.
What’s a Pound of Change Worth?
I’ve talked before about issues with using social media data to predict election outcomes. Yesterday Mashable’s 78th infographic of the day looks at a new wrench in the gears: spam:
The same techniques used by social spammers advertising free iPads and Viagra are now being used to spread bogus political messages across social media, blogs and news sites.
Yet another instance that should bring home that point that social media mention volume isn’t everything. A low-noise data source (likely coming from a tool with robust filtering capabilities) is the only way to reduce the impact of this nonsense on data quality.
This is the blog post-equivalent of arriving on the platform just as the 3 train arrives. Two items on NYC Subway data came on my radar this morning:
To that end, if the ongoing competition in computer security between those uncovering vulnerabilities and those patching vulnerabilities is any indication, these bots might be the initial glimmerings of a larger emerging competition between “truth black hats” — discovering and leveraging social exploits in groups online and “truth white hats” — developing the active infrastructure to “patch” these cognitive weaknesses in the same communities. Whether the strategic advantage in this space of “social security” (sorry) accrues to the astroturfer or to those attempting to block those efforts remains to be seen. —
I’m Not a Real Activist, But I Play One on the Internet | Truthiness in Digital Media
Here’s a Big Problem for practitioners of social listening to solve: what happens when the “people” responsible for Consumer-Generated Media aren’t actually people? Whether you’re taking a sample or analyzing in aggregate, the pool is contaminated.
Let’s End The Magical Thinking About Social Media ROI -
Conde Nast to Provide Ad Metrics for Tablets -
Forgive the Mashable link, but it gets the point across.
I didn’t have a chance to pick an NCAA bracket this year. I’m not too upset, as it means that my winning streak is intact (I won my office pool several years ago with a bracket titled “I actually hate Duke”). While they don’t account for the psychology of an office pool, I take a hard look at predictions from FiveThirtyEight’s Nate Silver and others before I complete my bracket.
This time of year is also exciting to mathematically-inclined sports fans because it means that MIT’s Sloan Business School hosts its Sports Analytics Conference.
Numbers can tell us a lot about technology, but only if we know them. Here are a few we don’t. —
The Numbers We Don’t Know
6 Surprising Pizza Pie Charts -
Happy π Day!
Predicting a story's popularity on Digg | Digg Topnews -
I’ve been playing with this car metaphor for using social media data. I really need to give it its own post. But the idea boils down to using it as a rearview mirror (that is, backward-looking), as a dashboard and a dipstick (seeing how you’re doing right now), and a windshield (seeing where you’re going). The first two are quite common, the third, much less so. But here’s a cool example.
Why Klout really matters: Money, money, money — GigaOm -
I’m going to skip past the “be wary of any black box algorithm” rant.
I assume many companies are already taking similar approaches to using Twitter as a marketing campaign. Step 1 might be finding out how people feel about a particular product, show, etc., by analyzing the Twitter firehose. But Step 2 should be finding out which Twitter users are influential in that space and trying to make them happy. Or maybe part of Step 1 is weighting sentiment based on who expressed it — an influential voice coming out in support of or against something might be worth more than someone with relatively low influence in determining how something will play out.
I’m totally on board with ideation as Step 1. Take a look at how people are talking and make sure your campaign reflects that language or content. But successful influencer outreach veers in a different direction after that. Take a look at who generates the content that promotes the most engagement, be it retweets, @mentions, link clicks, or traffic to your site (it’s always nice when you have some other channel data). And then see what those authors’ attitudes are to your brand/product/service. If they’re a fan, target them to your heart’s content. If they’re not, you should either avoid them or use this as an opportunity to convert them into one.
So if I were a GM, there are a number of data analysis projects that I think would be far more important that measuring fielding efficiency. I’d probably rather have a model to optimize farm system progression or predict deterioration curves for aging veterans. I’m willing to bet that with a combination of lifestyle, demographic, mechanical, and psychographic data, I could build a pretty good model of age deterioration that would significantly out-perform most GM’s mental math. Double that for farm system progression, where I suspect many organizations are markedly inefficient. An analysis that tackled either of these issues would, I’m guessing, be far, far more impactful than fielding efficiency for the organization. These problems might not yield sexy visualizations but they would yield true competitive advantage. —
Finding the Right Problem to Tackle: When Web Analytics Technologies Chase Problems - SemAngel
I recognize that the point of this post is measuring what matters in the digital space, but this section totally reads like the treatment for Moneyball 2. Somewhere Jonah Hill is getting ready for his second Oscar nomination.
So for 2012, we can expect campaigns to make use of aggregated structured data from their web sites, apps, records of volunteers canvassing and other traditional collection methods. They will also be collecting and analyzing unstructured data from interviews conducted with voters, social media and other sources to get a sense of how the public feels about issues. At the same time, they will try to get a more complete picture of the voter by merging offline and online identities. —
Vote for me: How data will change the 2012 elections — Cloud Computing News
I find it interesting how the tone here is so much more nonchanalant than it was for the New York Times piece on Target a little while ago
For one thing, digital skills are no longer a plus but expected. Mobile and social media are the two areas most in demand. Midlevel analytics jobs are a sweet spot, and media agencies need strategists focused on loyalty and content marketing. —
Wanted: Social, Mobile and Gaming Guru | News - Advertising Age
Sorry folks, I’m taken :-)