My reaction has been similar to yours Elijah. Yes. Social Media is fraught with bias, and yep, so are just about all other kinds of data. I do think it is somewhat unfortunate that the write up makes it sound like no one has thought about these data limitations. There are plenty AoIRers who have talked and even written critically about big data. Still, I think it's good that the paper came out. I do read plenty of 'big data' papers that gloss over or completely fail to mention any limitations. On Mon, Dec 1, 2014 at 11:32 AM, Elijah Wright <elijah.wright@gmail.com> wrote:

My reaction to that title is scorn and the impulse to point out that all data has biases, and statistics is how you turn raw data into insights about *the data*.

After a little more consideration, I think the press release is much more reasonable than the title makes it out to be. Yes, it's very common that people want to make it implicit that social media is a good representation of reality. It ain't necessarily so, folks! :) And a great big "Yes! That!" to inbuilt sampling bias and "black box" views of available data - these are very real problems that can come along with complexity and huge rivers of data. :)

(To reply to Peter Timusk's other reply to this post -- yes, lots of people write bad papers. Not worth worrying about, really. It's a shame, but... what can you do? Best thing is always to do our own, superlative work, and just be model citizens of the ecosystem :) )

best,

--e

On Sun, Nov 30, 2014 at 1:35 PM, Katja Mayer <katja.mayer@univie.ac.at> wrote:

...

FYI: http://www.sciencemag.org/content/346/6213/1063.summary

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 misleading. 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. _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org

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---

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-- Jeff Hemsley Assistant Professor School of Information Studies Syracuse University