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Modelling of trends in twitter using retweet graph dynamics

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Author: Thij, M. ten · Ouboter, T.M. · Worm, D.T.H. · Litvak, N. · Berg, J.L. van den · Bhulai, S.
Publisher: Springer Verlag
Place: Switzerland
Source:Bonato, A.Graham, F.C.Prałat, P., Algorithms and Models for the Web Graph - Proceedings 11th International Workshop, WAW 2014, December 17-18, 2014, Beijing, China., 132-147
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Identifier: 522540
doi: doi:10.1007/978-3-319-13123-8_11
Keywords: Informatics · Retweet graph · Behavioral research · Basic characteristics · Graph dynamics · Model parameters · Random graph models · Trending topics · Twitter · Social networking (online) · Infostructures · Information Society · Communication & Information · PNS - Performance of Networks & Services · TS - Technical Sciences


In this paper we model user behaviour in Twitter to capture the emergence of trending topics. For this purpose, we first extensively analyse tweet datasets of several different events. In particular, for these datasets, we construct and investigate the retweet graphs. We find that the retweet graph for a trending topic has a relatively dense largest connected component (LCC). Next, based on the insights obtained from the analyses of the datasets, we design a mathematical model that describes the evolution of a retweet graph by three main parameters.We then quantify, analytically and by simulation, the influence of the model parameters on the basic characteristics of the retweet graph, such as the density of edges and the size and density of the LCC. Finally, we put the model in practice, estimate its parameters and compare the resulting behavior of the model to our datasets.