  Twitter continuously tightens the access to its data via the publicly
accessible, cost-free standard APIs. This especially applies to the follow
network. In light of this, we successfully modified a network sampling method
to work efficiently with the Twitter standard API in order to retrieve the most
central and influential accounts of a language-based Twitter follow network:
the German Twittersphere. We provide evidence that the method is able to
approximate a set of the top 1 to 10 percent of influential accounts in the
German Twittersphere in terms of activity, follower numbers, coverage and
reach. Furthermore, we demonstrate the usefulness of these data by presenting
the first overview of topical communities within the German Twittersphere and
their network structure. The presented data mining method opens up further
avenues of enquiry, such as the collection and comparison of language-based
Twitterspheres other than the German one, its further development for the
collection of follow networks around certain topics or accounts of interest,
and its application to other online social networks and platforms in
conjunction with concepts such as agenda setting and opinion leadership.
