Social networks create poor information collection sources

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ENGLAND: Even though it is simple to suppose that the general public of any social network could represent a fair model size for collecting data sets, the reality is that no social network correctly represents any specific community.
Unfortunately scientists have been using data collected from social networks and publishing papers based on their findings. This means that what they though was an accurate estimation worthy of publication, was really a specific kind of bias based on incomplete information.
McGill University School of Computer Science assistant professor Derek Ruths explains “Many of these papers are used to inform and justify decisions and investments among the public and in industry and government.”
Ruths is joined by Carnegie Mellon Institute for Software Research staffer Jurgen Pfeffer for the article, which they published in the Nov 28 issue of the journal Science. They address several issues which involve the use of social media to collect data sets; and they also offer strategies to address these concerns.
The pair comments that many of these problems have quite well-known solutions from outside fields of study like epidemiology or statistics or machine learning. “The common threat 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 notes that social scientists have really focused their techniques and their standards in order to better deal with these complications. For example, “The infamous ‘Dewey Defeats Truman’ headline of 1948 stemmed from telephone surveys that under-sampled Truman supporters in the general population. Rather than permanently discrediting the 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.”