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

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