An MIT Sloan Management Review article caught my eye this week as I
was doing some research for my group project. The article, “The
Pitfalls of using online and social data in Big Data Analysis,”
discusses a provocative finding, made by two professors from Princeton and
North Carolina (Chapel Hill). They say that “inferences based on how people use
social media platforms like Twitter and Facebook should be reconsidered”
because they are “skewed samples.” In other words, data analysis on these
platforms has real issues in terms of veracity and applicability in assessing
and predicting social behavior. As such, this has important ramifications on
the digital marketing landscape, as it questions whether we can really rely
upon large scale social data analysis for advertising and marketing targeting and
segmentation.
Why does the research call Twitter and Facebook “skewed samples”?
Well, Twitter is used by 10% of the US population, but a pretty specific segment
(not a representative sample across all demographic groups) and Facebook users,
while a wider sample, are likewise not representative due to race, gender, and
class bias. In addition, the research questions the practice common today of measuring
and analyzing only actions that users
take (such as how many people “liked” a Facebook status update or retweeted a
message), with no corollary analysis to weigh how many people did not take that action. According to the
research, this kind of isolated, single-method research which is “partial,
filtered, distorted” may lead to not only misinterpretation but also fundamental
misunderstanding.
The bottom line? Big is not necessary better. Or as Princeton Professor Tufekci put it, “more data does not
necessarily mean more insight.” For would-be digital marketers, we need to
remember this when looking at large scale data analytics conducted on social media and make
sure we know the limitations of the data set being analyzed and the assumptions
that are being made to produce the results we seek.
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