Chuck Bednar for redOrbit.com Your Universe Online

Behavioral scientists and other academic researchers are increasingly turning to social media to find subjects for their studies, but doing so could lead to erroneous results with serious implications, computer experts from Carnegie Mellon University (CMU) in Pittsburgh and McGill University in Montreal report in a newly published study.

According to the authors of the paper, which was published in the November 28 edition of the journal Science, social media appears attractive to researchers behind behavioral studies because it gives them a quick and inexpensive way to gather massive amounts of data about peoples thoughts and feelings. Some of those dataset may be misleading, however, they explained.

In their paper, Carnegie Mellons Juergen Pfeffer and McGill Universitys Derek Ruths note that thousands of research papers each year are based on information gathered through social media. However, they contend that scientists need to find ways for correcting the inherent biases in information gathered from the likes of Facebook and Twitter, or at the very least acknowledge that there could be issues with such data.

Not everything that can be labeled as Big Data is automatically great, said Pfeffer, an assistant research professor in CMUs Institute for Software Research, explained in a statement. He said that while many researchers believe that if they can gather a large enough dataset, it will overcome any potential biases or distortions inherent in that data, but the old adage of behavioral research still applies: Know Your Data.

He and Ruths, an assistant professor of computer science at McGill, said that even though the problem is far from insignificant, social media is still difficult to resist as a source of data. People want to say something about whats happening in the world and social media is a quick way to tap into that, Pfeffer said. For example, following 2013s Boston Marathon bombing, he said he collected 25 million tweets related to the topic in just two weeks time.

The main problem, according to the researchers, is the attempt for study authors to generalize their results to a broad population. However, social media sites often have significant population biases in that different social networks attract different types of users. For example, Pinterests membership is primarily females aged 25 to 34 with average household incomes of $100,000, while Instagram appeals mostly to adults under the age of 29, African-Americans, Latinos, women and urban dwellers, Pfeffer and Ruths explained.

Other possible issues include the fact that publically available data feeds may not necessarily provide an accurate representation of the platforms overall data; the design of a social media platform may impact how users behave, and what behavior can be measured (for example, the lack of a dislike button on Facebook makes it harder to detect negative responses to content); and large numbers of bots and spammers may masquerade as human users, and thus their input may mistakenly be incorporated into behavior-related measurements and predictions.

Researchers often report results for groups of easy-to-classify users, topics, and events, making new methods seem more accurate than they actually are, McGill Universitys Chris Chipello explained. For instance, efforts to infer political orientation of Twitter users achieve barely 65 percent accuracy for typical users even though studies (focusing on politically active users) have claimed 90 percent accuracy.

The common thread in all these issues is the need for researchers to be more acutely aware of what theyre actually analyzing when working with social media data, Ruths noted, comparing the issue to the telephone survey errors that led to the infamous Dewey Defeats Truman headline during the Presidential election of 1948.

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28.11.2014 - (idw) Deutsches Primatenzentrum GmbH - Leibniz-Institut fr Primatenforschung

A Leibniz Science Campus Primate Cognition" will be established in Gttingen. On November 28, the Senate of the Leibniz Association has approved the necessary funding: For the next four years 900,000 Euros will be given annually to the research consortium. The founding members are the German Primate Center (DPZ), the University of Gttingen and the Bernstein Center for Computational Neuroscience (BCCN). In interdisciplinary projects, the scientists want to explore the cognitive abilities of monkeys and humans. The intensive cooperation should create a permanent, international competence network in this area of research. With the Leibniz Science Campus we would like to create a platform for interdisciplinary research in Gttingen, says Julia Fischer, head of the Cognitive Ethology Laboratory at the DPZ and spokesperson for the Science Campus. We want to especially strengthen cooperation within the field of primate cognition research, encourage young researchers and support new projects."

23 Gttingen behavioral biology, neuroscience and psychology researchers have already confirmed their participation. The official start will be on January 1st, 2015. The Science Campus is headed by a board, composed of two representatives of the DPZ and Gttingen University, respectively, as well as one representative of the BCCN. Besides Julia Fischer, the DPZ director Stefan Treue will represent the German Primate Center on this board. Together, the Leibniz Association, Gttingen University and the DPZ will each contribute one third of the budget.

Within multidisciplinary projects, the scientists will compare the mental abilities of human and non-human primates. This includes the complex processes of information intake, processing and turning the information into actions, as well as communication in monkeys and humans. "Above all, we are interested in the influence of social relationships on various thought processes and the resulting behavior," explains Julia Fischer. "The role of social cognition is therefore one of our main topics."

Research and international cooperation

Two scientists, one from the DPZ and the other from the University of Gttingen, coordinate each cluster. Hansjrg Scherberger, head of the Neurobiology Laboratory, Claudia Fichtel senior scientist in the Behavioral Ecology and Sociobiology Unit as well as Igor Kagan who is a junior research group leader in the Cognitive Neuroscience Laboratory will act as coordinators for the German Primate Center.

"We would also like to intensify international cooperation within the science campus", says Julia Fischer. A regular exchange through workshops and colloquia will take place, but also programs such as visits to other institutes and inviting guest scientists to Gttingen.

A special cooperation in the form of a study community for humanities and social science disciplines is planned with the Lichtenberg-Kolleg of the University of Gttingen. In order to strengthen the dialogue between the humanities and natural sciences, a group of young scientists who deal with philosophical aspects of cognitive research and reflect ethical questions will meet regularly in seminars.

"The successful acquisition of the science campus funding would not have been possible without the long-standing and excellent cooperation of university and non-university institutes within the Gttingen Campus," Stefan Treue summarizes. "We hope that in the long run we can build Gttingen into an international beacon in the field of primate cognition research."

The cooperation model of science campus

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From thought to action: Leibniz Science Campus established in Gttingen

PUBLIC RELEASE DATE:

27-Nov-2014

Contact: Byron Spice bspice@cs.cmu.edu 412-268-9068 Carnegie Mellon University @CMUScience

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. vStill, 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.

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Using social media for behavioral studies is cheap, fast, but fraught with biases