Social media data pose pitfalls for studying behaviour

A growing number of academic researchers are mining social media data to learn about both online and offline human behaviour. In recent years, studies have claimed the ability to predict everything from summer blockbusters to fluctuations in the stock market. But 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, according to computer scientists at McGill University in Montreal and Carnegie Mellon University in Pittsburgh. Such 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's School of Computer Science. In an article published in the Nov. 28 issue of the journal Science, Ruths and Jürgen Pfeffer of Carnegie Mellon's Institute for Software Research highlight several issues involved in using social media data sets - along with strategies to address them.