  To study the effects of Online Social Network (OSN) activity on real-world
offline events, researchers need access to OSN data, the reliability of which
has particular implications for social network analysis. This relates not only
to the completeness of any collected dataset, but also to constructing
meaningful social and information networks from them. In this multidisciplinary
study, we consider the question of constructing traditional social networks
from OSN data and then present several measurement case studies showing how
variations in collected OSN data affects social network analyses. To this end
we developed a systematic com parison methodology, which we applied to five
pairs of parallel datasets collected from Twitter in four case studies. We
found considerable differences in several of the datasets collected with
different tools and that these variations significantly alter the results of
subsequent analyses. Our results lead to a set of guidelines for researchers
planning to collect online data streams to infer social networks.
