NovelTM member Derek Ruths recently co-authored an article with Jürgen Pfeffer of Carnegie Mellon’s Institute for Software Research, which elucidates the potential hazards in mining social media data sets for academic research. Published in the November 28 issue of the journal Science, the article cautions against a generalized or superficial perspective when using social media for research purposes, which may result in erroneous results. As the trend in using social media data as a source for academic analysis increases, the effects become long-reaching: “Many of these papers are used to inform and justify decisions and investments among the public and in industry and government,” says Ruths, an assistant professor in McGill’s School of Computer Science. Ruths and Pfeffer reveal research-related issues such as researchers’ failure to recognize the possibility of demographically-generated bias when considering a data set; potential misrepresentation of social media’s overall platform due to limitations in publicly-available data feeds; researchers’ lack of awareness of how the design of social media platforms can influence user behaviour; and the potential for bots and spammers to be erroneously included into calculations measuring human behaviour. “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,” says Ruths.
Read the study here: http://www.sciencemag.org/content/346/6213/1063.summary