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median teen Facebook user has 300 friends. Girls and older teenagers tend have substantially larger friend networks than boys and younger teens.

while 70 percent of teens surveyed are Facebook friends with their parents, focus groups conducted by Pew show that this kind of interaction is the main problem for the age group. Teens felt like with mom and dad electronically close by, they couldn’t truly express themselves, opting for sites such as Twitter and Instagram: In focus groups, many teens expressed waning enthusiasm for Facebook. They dislike the increasing number of adults on the site, get annoyed when their Facebook friends share inane details, and are drained by the “drama” that they described as happening frequently on the site. The stress of needing to manage their reputation on Facebook also contributes to the lack of enthusiasm. Nevertheless, the site is still where a large amount of socializing takes place, and teens feel they need to stay on Facebook in order to not miss out.

Unlike many of their adult counterparts, teens feel they’re pretty good at managing their Facebook privacy settings. 60 percent of teens aged 12 through 17 in the study say they have their Facebook profile private, so only their friends can see it.

Facebook’s algorithm uses a number of factors to establish which posts should be shown to users. Previously called EdgeRank, the algorithm now has more than 1,000 contributing factors, but it still focuses on three main influences: affinity, weight, and Time.

Affinity is defined by a user’s relationship with the person or page that created the specific Facebook object — essentially how much the user interacts with that person or page.

Time, the last major factor, takes into account how recent the action occurred, which, in Facebook vernacular, is called time decay.

Altimeter Group recently studied the internal goals in corporate social strategy. The top priority stated by 48% of companies was “Creating ROI Measurements”. Hypatia Research showed management’s expectations of the return on social communities are rather low. Research by Chief Marketer shows that the number of likes, friends & followers are the most used metrics by 60% of U.S. B2C and B2B marketers.

There exists great controversy about the use of ‘reach’ metrics.

I noticed strong correlations between all of the metrics. This means that reach, amplification, conversations and sentiment appear to measure the same kind of digital influence.

The beauty of such Big Data applications is that they can process Web-based text, digital images, and online video. They can also glean intelligence from the exploding social media sphere, whether it consists of blogs, chat forums, Twitter trends, or Facebook commentary. Traditional market research generally involves unnatural acts, such as surveys, mall-intercept interviews, and focus groups. Big Data examines what people say about what they have done or will do. That's in addition to tracking what people are actually doing about everything from crime to weather to shopping to brands. It is only Big Data's capacity for dealing with vast quantities of real-time unstructured data that makes this possible.

the number of Google queries about housing and real estate from one quarter to the next turns out to predict more accurately what's going to happen in the housing market than any team of expert real estate forecasters. Similarly, Google search queries on flu symptoms and treatments reveal weeks in advance what flu-related volumes hospital emergency departments can expect.

Much of the data organizations are crunching is human-generated. But machine sensors — what GE people like CMO Beth Comstock called "machine whispering" when I talked with her this past summer — are creating a second tsunami of data. Digital sensors on industrial hardware like aircraft engines, electric turbines, automobiles, consumer packaged goods, and shipping crates can communicate "location, movement, vibration, temperature, humidity, and even chemical changes in the air."