[Air-L] Social Media data fraught with biases and distortion

Katja Mayer katja.mayer at univie.ac.at
Sun Nov 30 11:35:14 PST 2014


 From the press release:
Using Social Media For Large Behavioral Studies Is Fast and Cheap, But 
Fraught With Biases and Distortion

PITTSBURGH—The rise of social media has seemed like a bonanza for 
behavioral scientists, who have eagerly tapped the social nets to 
quickly and cheaply gather huge amounts of data about what people are 
thinking and doing. But computer scientists at Carnegie Mellon 
University and McGill University warn that those massive datasets may be 
In a perspective article published in the Nov. 28 issue of the journal 
Science, Carnegie Mellon’s Juergen Pfeffer and McGill’s Derek Ruths 
contend that scientists need to find ways of correcting for the biases 
inherent in the information gathered from Twitter and other social 
media, or to at least acknowledge the shortcomings of that data.

And it’s not an insignificant problem; Pfeffer, an assistant research 
professor in CMU’s Institute for Software Research, and Ruths, an 
assistant professor of computer science at McGill, note that thousands 
of research papers each year are now based on data gleaned from social 
media, a source of data that barely existed even five years ago.
“Not everything that can be labeled as ‘Big Data’ is automatically 
great,” Pfeffer said. He noted that many researchers think — or hope — 
that if they gather a large enough dataset they can overcome any biases 
or distortion that might lurk there. “But the old adage of behavioral 
research still applies: Know Your Data,” he maintained.
Still, social media is a source of data that is hard to resist. “People 
want to say something about what’s happening in the world and social 
media is a quick way to tap into that,” Pfeffer said. Following the 
Boston Marathon bombing in 2013, for instance, Pfeffer collected 25 
million related tweets in just two weeks. “You get the behavior of 
millions of people — for free.”

The type of questions that researchers can now tackle can be compelling. 
Want to know how people perceive e-cigarettes? How people communicate 
their anxieties about diabetes? Whether the Arab Spring protests could 
have been predicted? Social media is a ready source for information 
about those questions and more.
But despite researchers’ attempts to generalize their study results to a 
broad population, social media sites often have substantial population 
biases; generating the random samples that give surveys their power to 
accurately reflect attitudes and behavior is problematic. Instagram, for 
instance, has special appeal to adults between the ages of 18 and 29, 
African-Americans, Latinos, women and urban dwellers, while Pinterest is 
dominated by women between the ages of 25 and 34 with average household 
incomes of $100,000. Yet Ruths and Pfeffer said researchers seldom 
acknowledge, much less correct, these built-in sampling biases.

Other questions about data sampling may never be resolved because social 
media sites use proprietary algorithms to create or filter their data 
streams and those algorithms are subject to change without warning. Most 
researchers are left in the dark, though others with special 
relationships to the sites may get a look at the site’s inner workings. 
The rise of these “embedded researchers,” Ruths and Pfeffer said, in 
turn is creating a divided social media research community.
As anyone who has used social media can attest, not all “people” on 
these sites are even people. Some are professional writers or public 
relations representatives, who post on behalf of celebrities or 
corporations, others are simply phantom accounts. Some “followers” can 
be bought. The social media sites try to hunt down and eliminate such 
bogus accounts — half of all Twitter accounts created in 2013 have 
already been deleted — but a lone researcher may have difficulty 
detecting those accounts within a dataset.

“Most people doing real social science are aware of these issues,” said 
Pfeffer who noted that some solutions may come from applying existing 
techniques already developed in such fields as epidemiology, statistics 
and machine learning. In other cases, scientists will need to develop 
new techniques for managing analytic bias.

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