Last Wednesday’s issue of Science had Juergen Pfeffer and McGill’s Derek of Carnegie Mellon write about how the plausible biased nature of the data troves generated through social networks, which behavioural and social scientists might consider valuable, goes unchecked and uncorrected.
“Not everything that can be labelled as ‘Big Data’ is automatically great,” Pfeffer points out. The researchers’ notion that a fairly large dataset can ‘overcome any biases or distortion’ is not wholly true.
Pfeffer and Ruths study brings out the ‘lack of context’ in this data based on which innumerable papers are written yearly. “The amount of research done from Twitter is enormous!” Pfeffer said, reports Forbes. “For instance, search for “Twitter” on Google Scholar and you will get 4.9 million results. This is more than almost every other keyword possible, e.g. “Sociology” (2.5M).”
What complicates the scenario more is the issue of phantom accounts, which the social media does try to weed out regularly, but to know of them for a researcher on a smaller scale within the dataset can prove impossible, write to Ruths and Pfeffer.
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