Walking Through Twitter: Sampling a Language-Based Follow Network of Influential Twitter Accounts

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 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.