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The wide use of social media sites and other digital technologies haveresulted in an unprecedented availability of digital data that are being usedto study human behavior across research domains. Although unsolicited opinionsand sentiments are available on these platforms, demographic details areusually missing. Demographic information is pertinent in fields such asdemography and public health, where significant differences can exist acrosssex, racial and socioeconomic groups. In an attempt to address thisshortcoming, a number of academic studies have proposed methods for inferringthe demographics of social media users using details such as names, usernames,and network characteristics. Gender is the easiest trait to accurately infer,with measures of accuracy higher than 90 percent in some studies. Race,ethnicity and age tend to be more challenging to predict for a variety ofreasons including the novelty of social media to certain age groups and a lackof significant deviations in user details across racial and ethnic groups.Although the endeavor to predict user demographics is plagued with ethicalquestions regarding privacy and data ownership, knowing the demographics in adata sample can aid in addressing issues of bias and population representation,so that existing societal inequalities are not exacerbated.

How well can machine learning predict demographics of social media users?