Home' Technology Review : November December 2011 Contents Feature Story 49
that?’” says Roy. “Well, those were the ads.” It hadn’t occurred to
them that people would talk about ads. But they do. They write—
as one recently did, in a tweet picked up by Bluefin—things like
“The dude rappin in the mcdonalds commercial about the smooth-
ies will forever be clowned where ever he goes.”
Roy and Fleischman realized that the advertising industry might
be interested in understanding more about such comments, and
advertisers had large research budgets. “We took the principles of
big data, data mining, and visualization,” Roy says, “and turned that
microscope [in my house] into a telescope to look at the world of
social media as it relates to television.” They called their work the
“TV genome.” Today Bluefin has 15 clients, including Pepsi, Mars,
and Comcast; the TV networks CBS, Fox Sports, A+E Networks,
AMC Networks, and Turner Broadcasting; and the ad agencies
McGarryBowen and Hill Holliday. The company business is selling
subscriptions to its interface and custom analytics. While making
these conquests, Roy encountered a language learning issue of his
own. “When I started talking to people in TV, I’d hear the word
‘programming.’ Turns out they weren’t talking about programming
software,” he recalls. “It took me a while to figure this out.”
InsIde the telescope
In order to capture almost everything happening on television,
Bluefin uses a data center studded with satellite dishes in Medford,
Massachusetts (see “Heeding the Tweets,” next page). Through the
first week of October, they’d pulled in every minute of more than
210,000 episodes of 7,100 television shows, plus advertisements.
The company now monitors 200 networks.
After uploading the raw feed to Amazon’s cloud computing ser-
vice, Bluefin gathers programming-guide information—the names
of the shows, their broadcast channels and times, and also the
names of characters and actors—along with closed-captioning
text extracted from the video signal itself. This provides a list of
keywords that can help identify relevant social-media comments.
Since advertising schedules are not published ahead of time, Blue-
fin creates one. The algorithm detects when a “pod” of ads has
started. Then a system of digital fingerprinting identifies repeat
airings; human staffers are notified of first-time airings to make
the initial identification.
Among the more than 10 million comments made daily about
TV content, Bluefin’s algorithms identify about 1.4 million that are
made in the three hours before or after a show or advertisement
aired on one of the networks it tracks. (About 90 percent of these
comments are tweets; the bulk of the remainder are public Face-
book posts.) Although on-demand services, recording technologies,
and new Internet models of TV delivery are changing viewing hab-
its (see “Searching for the Future of Television,” January/February
2011), most people still watch television the old-fashioned way, and
Roy says they seem more likely to make real-time comments when
they know they are watching the first airing. Bluefin also keeps
close tabs on the 9.8 million people who have commented about
television at least once in the past 90 days, to build up knowledge
about their demographics and interests.
Text analysis underpins all these efforts: whereas “delicious” or
“tasty” might indicate a positive response to a restaurant, terms like
“can’t wait” or “fascinating” or “drivel” might show up in comments
related to TV shows. Bluefin is working on identifying not only
positive or negative reactions but ones that are vulgar or polite,
serious or amused, calm or excited. “At the highest level, what we
are trying to do is language understanding,” Fleischman says. It also
tries to glean demographic information about who is commenting.
Women, for example, are more likely to refer to family members,
while men are more likely to mention friends or electronic devices.
Emoticons hint at age: someone who uses :-) is probably 10 years
older than someone who uses :). People using 8-) are even older.
Bluefin ultimately turns all this data into two main measure-
ments. “Response level” reports the number of people commenting
on any given ad or episode of a show, measured on a logarithmic
10-point scale. “Response share” measures what percentage of all
social-media response to television programming at a given air-
time focused on a particular show or ad. The company’s first inter-
face—Bluefin Signals, which provides analytics on comments about
TV shows—went live in June. A second, which is due for release in
December, will track response to individual ad campaigns. Next year
Bluefin plans to include Spanish-language comments in its analysis.
Network eFFect David Poltrack, chief research officer for cBS, has long
recognized the value of viewer conversations about shows. Now he’s evalu-
ating tools that scrutinize millions of comments made about tV online.
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