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% 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.

Twitter不断通过可公开访问的免费标准API加强对数据的访问。这尤其适用于跟随网络。有鉴于此，我们成功地修改了一种网络采样方法，以便与Twitter标准API一起有效地工作，以便检索基于语言的Twitter追踪网络（德国Twittersphere）的最核心和最具影响力的帐户。我们提供的证据表明，根据活动，关注者人数，覆盖范围和覆盖范围，该方法能够近似估算德国Twittersphere中影响力最高的帐户的前1％至10％的一组。此外，我们通过介绍德国Twittersphere内的主题社区及其网络结构的第一个概述来证明这些数据的有用性。提出的数据挖掘方法开辟了进一步的查询渠道，