Over a million developers have joined DZone.
{{announcement.body}}
{{announcement.title}}

Data News: Facebook Data Cluster Predicts Break-Ups, and More

DZone's Guide to

Data News: Facebook Data Cluster Predicts Break-Ups, and More

· Big Data Zone
Free Resource

Learn best practices according to DataOps. Download the free O'Reilly eBook on building a modern Big Data platform.

Some writings worth reading:

…journalism has adopted the academic publishing model, only without the pretense of integrity. The 2008 economic crisis, combined with the transition to digital media, led to a glut of desperate writers willing to work for free—a practice that media corporations embraced and repackaged to novice journalists as “the way things have always been.” Today media outlets making healthy profits refuse to pay the freelance writers who help make them a success. Exploitative publishers tend to argue along two lines: a fake crisis (“Unfortunately, we can’t afford to pay you at this time…”) or a false promise (“Exposure will help your career.”). Academics are particularly vulnerable to media-industry exploitation. They are accustomed to writing for nothing and, in the case of adjuncts, to being treated terribly by their employers. Because academic work in professional journals is hidden behind paywalls, the prospect of reaching a wider audience can be enticing. For scholars interested in leaving academia and forging a new career, online visibility is essential. Should academics ever write for free? Maybe. Should academics write for free for a publisher that can afford to pay them? Never. [to be continued...]

In the graphic of one person’s network neighborhood (above), the cluster at the top is the individual’s co-workers. The cluster at the right is old college friends. The node (friend) in the lower left quadrant of the graphic, with links to the two dense clusters — but at a distance from those clusters — is the user’s spouse. “A spouse or romantic partner is a bridge between a person’s different social worlds,” Mr. Kleinberg explained in an interview on Sunday. Their dispersion algorithm was able to correctly identify a user’s spouse 60percent of the time, or better than a 1-in-2 chance. Since everyone in the sample had at least 50 friends, merely guessing would have at best produced a 1 in 50 chance. The algorithm also did pretty well with people who declare themselves to be “in a relationship,” correctly identifying them a third of the time — a 1 in 3 chance compared with the 1 in 50 for guesswork. Particularly intriguing is that when the algorithm fails, it looks as if the relationship is in trouble. A couple in a declared relationship and without a high dispersion on the site are 50 percent more likely to break up over the next two months than a couple with a high dispersion, the researchers found. (Their research tracked the users every two months for two years.) [to be continued...]

“I’m just not a math person.” We hear it all the time. And we’ve had enough. Because we believe that the idea of “math people” is the most self-destructive idea in America today. The truth is, you probably are a math person, and by thinking otherwise, you are possibly hamstringing your own career. Worse, you may be helping to perpetuate a pernicious myth that is harming underprivileged children—the myth of inborn genetic math ability. Is math ability genetic? Sure, to some degree. Terence Tao, UCLA’s famous virtuoso mathematician, publishes dozens of papers in top journals every year, and is sought out by researchers around the world to help with the hardest parts of their theories. Essentially none of us could ever be as good at math as Terence Tao, no matter how hard we tried or how well we were taught. But here’s the thing: We don’t have to! For high school math, inborn talent is just much less important than hard work, preparation, and self-confidence. [...]  Too many Americans go through life terrified of equations and mathematical symbols. We think what many of them are afraid of is “proving” themselves to be genetically inferior by failing to instantly comprehend the equations (when, of course, in reality, even a math professor would have to read closely). So they recoil from anything that looks like math, protesting: “I’m not a math person.” And so they exclude themselves from quite a few lucrative career opportunities. We believe that this has to stop. Our view is shared by economist and writer Allison Schrager, who has written two wonderful columns in Quartz (here and here), that echo many of our views. [to be continued...]

Find the perfect platform for a scalable self-service model to manage Big Data workloads in the Cloud. Download the free O'Reilly eBook to learn more.

Topics:

Published at DZone with permission of Arthur Charpentier, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}