IN THE 1930S AND ’40S, Hollywood had a way of tracking the popularity of its movie stars. Studios would sift through the quarter of a million or so fan letters that arrived each month and sort them into separate bags by actor name. Then studio employees would heave these bulging bags onto a scale, according to industry researcher Leo Handel. A big spike in weight meant the star was trending up. A sharp decline suggested the star was on the way to becoming yesterday’s news.
As measurements go, this was pretty crude. Even back then, the people who would take the time to write a letter represented a tiny subset of the population, usually teenagers motivated by an excess of adoration (or antipathy). So, in time, movie executives would follow the lead of their counterparts in radio, television, and advertising and adopt the techniques of opinion research to understand what their audiences wanted.
The push toward data collection in television brought us Nielsen families, those chosen few whose living room diaries and, eventually, People Meters were powerful enough to keep their favorite shows on the air. And it brought us the ubiquitous focus group, where a dozen unhurried souls would be steered into a conference room and, in exchange for 50 bucks and all the M&M’s they could eat, be asked to render a verdict on a new program.
Yet it hasn’t always been clear how much we’ve gained from this relentless pursuit of audience preferences. High Nielsen ratings — scores that were extrapolated from just several thousand households — kept shows like Three’s Company and The Love Boat on the air long past their sell-by dates. And I’ve been suspicious of the focus group ever since the seventh grade, when on a trip to New York I somehow got shanghaied into testing a sitcom starring Harold Gould as a skirt-chasing widower. Against the heated objection of this 13-year-old out-of-towner, CBS went ahead and aired Foot in the Door in 1983, though the network thankfully mercy-killed it after just six episodes. What’s the point of market research if it regularly leads to doozies like that? We might as well bring back the fan-mail scales.
In a way, that’s what’s happening.
Thanks to social media, thousands of fan letters and complaint missives, huzzahs and boos, are now being written every single minute. Twitter alone processes half a billion tweets each day. But there are problems. As with those letter-writing fans of the past, today’s social media commenters skew young. And right now, the most common methods for tracking their views resemble the “trending by weight” measures the old studios favored: Multiple firms tally all mentions of a TV show or movie made on social media, then report grand totals across general categories. This is useful only to a point.
Twitter, Facebook, and other services have already transformed media from a one-way conversation into a democratized, constantly churning feedback loop. In time, social media hold the promise of exercising enormous influence over everything from the shows we watch on TV to the toothpaste we buy in the supermarket to the politicians we send to Washington. “Social TV” and the “second screen” experience — watching the TV set while cradling a smartphone or tablet — may even rescue live television viewing from the dustbin into which the DVR has swept it.
Yet the only way any of this is going to happen is if somebody can reliably convert all that online chatter into meaningful information. After all, if someone tweets “the office is making me cry,” is that person referring to a particularly poignant episode of the NBC comedy or a hostile workplace? Even more difficult is discerning sentiment. Are most of those millions of mentions about your show praising it or panning it? And what if the name of the show isn’t even mentioned? Making those kinds of interpretations are easy for humans yet exceedingly difficult for computers.
But they’re learning. A Cambridge start-up called Bluefin Labs is marrying the computational power of machines with the interpretive guidance of humans to make sense of — and profit from — the fire hose of nonstop social media. The company’s work builds on the research of its two cofounders, MIT guys who have dedicated their professional lives to teaching machines to understand human language. Now they are using that knowledge to teach machines to understand what we really mean when we tweet or post about everyone from President Obama to Honey Boo Boo. The outcome just may be as important to the president as it is to that cringe-worthy pint-size product of reality TV. Continued...