5 ways social media data can be risky

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," says Jürgen Pfeffer. (Credit: "man on charts" via Shutterstock)

Researchers are turning to social media data to study human behavior both on- and offline, such as predicting summer blockbusters or fluctuations in the stock market.

Evidence of flaws in many of these studies points to a need for researchers to be wary of serious pitfalls that arise when working with huge social media data sets, according to computer scientists.

Erroneous results can have huge implications: thousands of research papers each year are now based on data gleaned from social media. “Many of these papers are used to inform and justify decisions and investments among the public and in industry and government,” says Derek Ruths, an assistant professor in McGill University’s School of Computer Science.

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“Not everything that can be labeled as ‘Big Data’ is automatically great,” says coauthor Jürgen Pfeffer of Carnegie Mellon University’s Institute for Software Research.

He notes 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 maintains.

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 says.

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.”

Filters and bots

In a commentary published in Science, Ruths and Pfeffer highlight several issues involved in using social media data sets, along with strategies to address them. Among the challenges:

  • Different social media platforms attract different users—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 say researchers seldom acknowledge, much less correct, these built-in sampling biases.
  • Publicly available data feeds used in social media research don’t always provide an accurate representation of the platform’s overall data—and researchers are generally in the dark about when and how social media providers filter their data streams.
  • The design of social media platforms can dictate how users behave and, therefore, what behavior can be measured. For instance, on Facebook the absence of a “dislike” button makes negative responses to content harder to detect than positive “likes.”
  • Large numbers of spammers and bots, which masquerade as normal users on social media, get mistakenly incorporated into many measurements and predictions of human behavior.
  • Researchers often report results for groups of easy-to-classify users, topics, and events, making new methods seem more accurate than they actually are. 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.

Solutions are out there

Many of these problems have well-known solutions from other fields such as epidemiology, statistics, and machine learning, Ruths and Pfeffer write.

“The common thread in all these issues is the need for researchers to be more acutely aware of what they’re actually analyzing when working with social media data,” Ruths says.

Social scientists have honed their techniques and standards to deal with this sort of challenge before.

“The infamous ‘Dewey Defeats Truman’ headline of 1948 stemmed from telephone surveys that under-sampled Truman supporters in the general population,” Ruths notes.

“Rather than permanently discrediting the practice of polling, that glaring error led to today’s more sophisticated techniques, higher standards, and more accurate polls. Now, we’re poised at a similar technological inflection point. By tackling the issues we face, we’ll be able to realize the tremendous potential for good promised by social media-based research.”

Sources: McGill University, Carnegie Mellon University