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A pattern-based Approach for an Early Detection of Popular Twitter Accounts

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Abstract

Social networks (SN) are omnipresent in our lives today. Not all users have the same behaviour on these networks. If some have a low activity, rarely posting messages and following few users, some others at the other extreme have a signicant activity, with many followers and regularly posts. The important role of these popular SN users makes them the target of many applications for example for content monitoring or advertising. It is therefore relevant to be able to predict as soon as possible which SN users will become popular. In this work, we propose a technique for early detection of such users based on the identication of characteristic patterns. We present an index, 2 ", which allows a scaling up of our approach to large social networks. We also describe our rst experiments that conrm the validity of our approach. CCS CONCEPTS • Information systems ! Social networks. KEYWORDS Twitter, popularity detection, pattern matching ACM Reference Format: Jonathan Debure, Stephan Brunessaux, Camelia Constantin, and Cédric du Mouza. 2020. A Pattern-based Approach for an Early Detection of Popular Twitter
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hal-02936179 , version 1 (11-09-2020)

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Jonathan Debure, Stephan Brunessaux, Camelia Constantin, Cédric Du Mouza. A pattern-based Approach for an Early Detection of Popular Twitter Accounts. International Database Engineering & Applications Symposium (IDEAS), Aug 2020, Séoul, France. ⟨10.1145/3410566.3410600⟩. ⟨hal-02936179⟩
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