  The discovery of phenomena in social networks has prompted renewed interests
in the field. Data in social networks however can be massive, requiring
scalable Big Data architecture. Conversely, research in Big Data needs the
volume and velocity of social media data for testing its scalability. Not only
so, appropriate data processing and mining of acquired datasets involve complex
issues in the variety, veracity, and variability of the data, after which
visualisation must occur before we can see fruition in our efforts. This
article presents topical, multimodal, and longitudinal social media datasets
from the integration of various scalable open source technologies. The article
details the process that led to the discovery of social information landscapes
within the Twitter social network, highlighting the experience of dealing with
social media datasets, using a funneling approach so that data becomes
manageable. The article demonstrated the feasibility and value of using
scalable open source technologies for acquiring massive, connected datasets for
research in the social sciences.
