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Twitter is a gold mine to linguists and those who study pop-culture to see how people describe things in real time.

Twitter is many things to many people, but lately it has been a gold mine for scholars in fields like linguistics, sociology and psychology who are looking for real-time language data to analyze.

Twitter’s appeal to researchers is its immediacy — and its immensity. Instead of relying on questionnaires and other laborious and time-consuming methods of data collection, social scientists can simply take advantage of Twitter’s stream to eavesdrop on a virtually limitless array of language in action.

At the University of Texas, for example, a group of linguists and social psychologists has been monitoring Twitter to track on-the-ground sentiment over the course of the Arab Spring, particularly in Egypt and Libya. After the death of Colonel Qaddafi, the linguist David Beaver and his assistants quickly summoned thousands of Arabic-language tweets before and after the event. They zeroed in on messages known to be from Libya by using Twitter’s system of geocoding. (Posts from cellphones, for instance, very often encode the user’s geographic coordinates.) The tweets were then automatically translated from Arabic to English and fed into a text-analysis computer program.

Twitterology, I love it!

In this burgeoning field of Twitterology, moods are also being gauged on a more global level. Two sociologists at Cornell University, Scott A. Golder and Michael W. Macy, recently published a study in the journal Science that looked at how emotions may relate to the rhythms of daily life, across many English-speaking countries. They observed a gradual falloff in positive terms from the beginning of the workday, bottoming out in the late afternoon.

One criticism of “sentiment analysis,” as such research is known, is that it takes a naïve view of emotional states, assuming that personal moods can simply be divined from word selection. This might seem particularly perilous on a medium like Twitter, where sarcasm and other playful uses of language often subvert the surface meaning.

James W. Pennebaker, a social psychologist at the University of Texas who pioneered the text-analysis program often used in this kind of research, warns that positive and negative emotion words are the “low-hanging fruit” in such studies, and that deeper linguistic analysis should be explored to provide a “richer, more nuanced view” of how people present themselves to the world.

Is this a nudge or a shove? A gloved fist sounds so, so harst.

As i originally asked, what could go wrong? Apparently, a lot:

But the research is arousing concern about possible unintended effects on public health and the environment, because once genetically modified insects are released, they cannot be recalled.

Ribstein responds to a piece by Jordan Weissman in the Atlantic critical of deregulation of lawyers. Weissman argues that with regulation, there will be more lawyers.

But don’t we already have too many lawyers? As The Times and other papers have amply documented, the U.S. currently faces a serious glut of attorneys, many of whom are finding it nearly impossible to get work as firms see profits shrink and governments face tighter budgets. Winston and Crandall have a ready retort. By bringing down prices, demand for services will increase, and that will create more legal jobs…

Letting more people become lawyers won’t drive down costs in high-flying corporate law. And although it could help control legal fees for the rest of us, we could wind up allowing under-educated people to represent important cases for families who can’t afford the high-flying treatment.

Ribstein disagrees:

The likely consequences of meaningful deregulation will not be more incompetent lawyers.  Rather, lawyers will have to prove their value against an array of technologies and information services that make legal advice cheaper and more accessible to ordinary consumers.  Whether or not they continue to be licensed, lawyers will have to get better in order not to be replaced by machines.  Licensing may remain, but its domain will shrink.  The changes throughout the existing profession are likely to be vast.

This article seems to echo a point Malcolm Gladwell made in Outliers. Using Bill Gates as an example (as did Outliers) the article shows that the key to Gates’s ascendancy was his voracious work ethic, and not merely fortunately circumstances.

Mr. Gates may have been a math and computer whiz kid at a top college that had computers in 1975, but he wasn’t the only math and computer whiz kid at Harvard, Stanford, Princeton, Yale, M.I.T., Caltech, Carnegie Mellon, Berkeley, U.C.L.A., the University of Chicago, Georgia Tech, Cornell, Dartmouth, Southern Cal, Columbia, Northwestern, Penn, Michigan or any number of other top colleges with comparable or even better computer resources.

Mr. Gates wasn’t the only person who knew how to program in Basic; the language was developed a decade earlier by Dartmouth professors, and it was widely known by 1975, used in academics and industry. And what about all the master’s and Ph.D. students in electrical engineering and computer science who had even more computer expertise than Mr. Gates on the day the Popular Electronics article appeared? Any could have decided to abandon their studies and start a personal computer software company. And computer experts already working in industry and academia could have done the same.

But how many of them changed their life plans — and cut their sleep to near zero, essentially inhaling food so as not to let eating interfere with work — to throw themselves into writing Basic for the Altair? How many defied their parents, dropped out of college and moved to Albuquerque to work with the Altair? How many had Basic for the Altair written, debugged and ready to ship before anyone else?

Thousands of people could have done the same thing that Mr. Gates did, at the same time. But they didn’t.

The difference between Mr. Gates and similarly advantaged people is not luck. Mr. Gates went further, taking a confluence of lucky circumstances and creating a huge return on his luck. And this is the important difference.

Luck, good and bad, happens to everyone, whether we like it or not. But when we look at the 10Xers, we see people like Mr. Gates who recognize luck and seize it, leaders who grab luck events and make much more of them.

This ability to achieve a high ROL at pivotal moments has a huge multiplicative effect for 10Xers. They zoom out to recognize when a luck event has happened and to consider whether they should let it disrupt their plans. Imagine if Mr. Gates had said to Paul Allen after seeing the Popular Electronics article: “Well, Paul, I’m kind of focused on my studies here at Harvard right now. Let’s wait a few years, and then I’ll be ready to start.”

I don’t believe in luck. I believe in hard work creating the opportunities that allow me to recognize even better opportunities. One of the things that kills me about my present employment–and I love my job–is I am so limited with my free time. Once my full time job becomes my full time job, watch out.