TL;DR
This paper introduces an efficient sampling method for identifying influential accounts in the German Twitter follow network using standard API access, enabling analysis of community structures and influential users.
Contribution
The authors adapted a network sampling technique to work with Twitter's standard API, allowing for the identification of top influential accounts in the German Twittersphere.
Findings
Successfully approximated top 1-10% influential accounts
Mapped topical communities within the German Twittersphere
Demonstrated the method's potential for cross-language and topic-specific studies
Abstract
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…
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