TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Identifying communicator roles in twitter
Proceedings of the 21st international conference companion on World Wide Web
Increasing message relevance in social networks via context-based routing
Proceedings of the 3rd international workshop on Modeling social media
Synthesis ranking with critic resonance
Proceedings of the 3rd Annual ACM Web Science Conference
Mixing methods and theory to explore web activity
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Exploring celebrity dynamics on Twitter
Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
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There are currently over 175 million Twitter accounts worldwide, making Twitter one of the most popular and most observed Social Media platform. But Twitter is not so much a social network where the exchange of personal information is facilitated -- in fact, recent surveys state that it's not very social at all with a large amount of inactive accounts and a low motivation of engaging in dialogues [1]. Twitter has rather evolved into a pool of constantly updating information streams consisting of links, short status updates, and eyewitness news. Among the millions of users, a small percentage is what is called the group of influencers or alpha users. They have a large, active audience that consumes and multiplies the content published by the influencer. Thus, an influencer's content -- whether it is plain text or links -- is distributed in a number of micro-networks and receives attention from a large amount of users even though they might not even be direct followers of the influencer. The further the content is spread, the further the influence of the user reaches. There are various tools that enable performance measurement on Social Media. Some only sum up numbers such as the amount of followers or mentions gained on Twitter; others interpret the numbers and rate the performance using a specific algorithm. An example for the latter is Klout, a popular service that will be looked at more closely, focusing on the question of how Klout calculates its scores which serve as a means of measuring success of Twitter usage. The research purpose of this paper is to determine a grounded approach for measuring social networking potential of individual Twitter users.