  Using social media data for statistical analysis of general population faces
commonly two basic obstacles: firstly, social media data are collected for
different objects than the population units of interest; secondly, the relevant
measures are typically not available directly but need to be extracted by
algorithms or machine learning techniques. In this paper we examine and
summarise two existing approaches to statistical analysis based on social media
data, which can be discerned in the literature. In the first approach, analysis
is applied to the social media data that are organised around the objects
directly observed in the data; in the second one, a different analysis is
applied to a constructed pseudo survey dataset, aimed to transform the observed
social media data to a set of units from the target population. We elaborate
systematically the relevant data quality frameworks, exemplify their
applications, and highlight some typical challenges associated with social
media data.
