  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 a measurement case study showing how the
reliability of OSN data affects social network analyses. To this end we
developed a systematic comparison methodology, which we applied to two parallel
datasets we collected from Twitter. We found considerable differences in
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.
