  In this paper we present a technique to couple non-traditional data with
statistics based on survey data, in order to partially correct for the bias
produced by non-random sample selections. All major social media platforms
represent huge samples of the general population, generated by a self-selection
process. This implies that they are not representative of the larger public,
and there are problems in extrapolating conclusions drawn from these samples to
the whole population. We present an algorithm to integrate these massive data
with ones coming from traditional sources, with the properties of being less
extensive but more reliable. This integration allows to exploit the best of
both worlds and reach the detail of typical "big data" sources and the
representativeness of a carefully designed sample survey.
