Social media data pose pitfalls to assess human behaviour

A new study has revealed that social media data should not be used to gauge human behaviour or trends because they are too biased and researchers are urged to hone methods for mining the data.

According to computer scientists at McGill University in Montreal and Carnegie Mellon University in Pittsburgh, mounting 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.

The research highlighted 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 - Pinterest, for example, has been dominated by females aged 25-34 - yet researchers rarely correct for the distorted picture these populations can produce.

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.

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

Derek Ruths, an assistant professor in McGill's School of Computer Science, said that the common thread in all these issues was the need for researchers to be more acutely aware of what they're actually analyzing when working with social media data.

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, which under-sampled Truman supporters in the general population, he further added.

The researcher concluded that 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, they're poised at a similar technological inflection point. By tackling the issues people face, they'll be able to realize the tremendous potential for good promised by social media-based research.

The study is published in the journal Science.