Abstract

The problem of clustering content in social media has pervasive applications, including the identification of discussion topics, event detection, and content recommendation. Here, we describe a streaming framework for online detection and clustering of memes in social media, specifically Twitter. A pre-clustering procedure, namely protomeme detection, first isolates atomic tokens of information carried by the tweets. Protomemes are thereafter aggregated, based on multiple similarity measures, to obtain memes as cohesive groups of tweets reflecting actual concepts or topics of discussion. The clustering algorithm takes into account various dimensions of the data and metadata, including natural language, the social network, and the patterns of information diffusion. As a result, our system can build clusters of semantically, structurally, and topically related tweets. The clustering process is based on a variant …

Date

2014

Authors

Mohsen JafariAsbagh, Emilio Ferrara, Onur Varol, Filippo Menczer, Alessandro Flammini

Journal

Social Network Analysis and Mining

Volume

4

Issue

237

Pages

1-13

Publisher

Springer Vienna