  Analysts and social scientists in the humanities and industry require
techniques to help visualize large quantities of microblogging data. Methods
for the automated analysis of large scale social media data (on the order of
tens of millions of tweets) are widely available, but few visualization
techniques exist to support interactive exploration of the results. In this
paper, we present extended descriptions of ThemeCrowds and SentireCrowds, two
tag-based visualization techniques for this data. We subsequently introduce a
new list equivalent for both of these techniques and present a number of case
studies showing them in operation. Finally, we present a formal user study to
evaluate the effectiveness of these list interface equivalents when comparing
them to ThemeCrowds and SentireCrowds. We find that discovering topics
associated with areas of strong positive or negative sentiment is faster when
using a list interface. In terms of user preference, multilevel tag clouds were
found to be more enjoyable to use. Despite both interfaces being usable for all
tested tasks, we have evidence to support that list interfaces can be more
efficient for tasks when an appropriate ordering is known beforehand.
