Modelling of trends in Twitter using retweet graph dynamics

Marijn Ten Thij; Tanneke Ouboter; Daniël Worm; Nelli Litvak; Hans Leo van den Berg; Sandjai Bhulai

doi:10.1007/978-3-319-13123-8_11

Modelling of trends in Twitter using retweet graph dynamics

Marijn Ten Thij, Tanneke Ouboter, Daniël Worm, Nelli Litvak, Hans Leo van den Berg, Sandjai Bhulai

Stochastic Operations Research

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

15 Citations (Scopus)

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Abstract

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.

Original language	English
Title of host publication	Algorithms and Models for the Web Graph
Subtitle of host publication	11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings
Editors	Anthony Bonata, Fan Chung Chung, Pawel Pralat
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	132-147
Number of pages	16
ISBN (Electronic)	978-3-319-13123-8
ISBN (Print)	978-3-319-13123-8
DOIs	https://doi.org/10.1007/978-3-319-13123-8_11
Publication status	Published - 17 Dec 2014
Event	11th International Workshop Algorithms and Models for the Web Graph, WAW 2014 - Beijing, China Duration: 17 Dec 2014 → 18 Dec 2014 Conference number: 11

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer International Publishing
Volume	8882
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Workshop

Workshop	11th International Workshop Algorithms and Models for the Web Graph, WAW 2014
Abbreviated title	WAW
Country/Territory	China
City	Beijing
Period	17/12/14 → 18/12/14

Keywords

EWI-25535
Twitter
Random graph model
METIS-309800
Retweet graph
IR-93646
Graph dynamics

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10.1007/978-3-319-13123-8_11

Cite this

Ten Thij, M., Ouboter, T., Worm, D., Litvak, N., van den Berg, H. L., & Bhulai, S. (2014). Modelling of trends in Twitter using retweet graph dynamics. In A. Bonata, F. C. Chung, & P. Pralat (Eds.), Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings (pp. 132-147). (Lecture Notes in Computer Science; Vol. 8882). Springer. https://doi.org/10.1007/978-3-319-13123-8_11

Ten Thij, Marijn ; Ouboter, Tanneke ; Worm, Daniël et al. / Modelling of trends in Twitter using retweet graph dynamics. Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings. editor / Anthony Bonata ; Fan Chung Chung ; Pawel Pralat. Cham, Switzerland : Springer, 2014. pp. 132-147 (Lecture Notes in Computer Science).

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title = "Modelling of trends in Twitter using retweet graph dynamics",

abstract = "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.",

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author = "{Ten Thij}, Marijn and Tanneke Ouboter and Dani{\"e}l Worm and Nelli Litvak and {van den Berg}, {Hans Leo} and Sandjai Bhulai",

year = "2014",

month = dec,

day = "17",

doi = "10.1007/978-3-319-13123-8_11",

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Ten Thij, M, Ouboter, T, Worm, D, Litvak, N, van den Berg, HL & Bhulai, S 2014, Modelling of trends in Twitter using retweet graph dynamics. in A Bonata, FC Chung & P Pralat (eds), Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings. Lecture Notes in Computer Science, vol. 8882, Springer, Cham, Switzerland, pp. 132-147, 11th International Workshop Algorithms and Models for the Web Graph, WAW 2014, Beijing, China, 17/12/14. https://doi.org/10.1007/978-3-319-13123-8_11

Modelling of trends in Twitter using retweet graph dynamics. / Ten Thij, Marijn; Ouboter, Tanneke; Worm, Daniël et al.
Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings. ed. / Anthony Bonata; Fan Chung Chung; Pawel Pralat. Cham, Switzerland: Springer, 2014. p. 132-147 (Lecture Notes in Computer Science; Vol. 8882).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Modelling of trends in Twitter using retweet graph dynamics

AU - Ten Thij, Marijn

AU - Ouboter, Tanneke

AU - Worm, Daniël

AU - Litvak, Nelli

AU - van den Berg, Hans Leo

AU - Bhulai, Sandjai

N1 - Conference code: 11

PY - 2014/12/17

Y1 - 2014/12/17

N2 - 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.

AB - 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.

KW - EWI-25535

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KW - Random graph model

KW - METIS-309800

KW - Retweet graph

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M3 - Conference contribution

SN - 978-3-319-13123-8

T3 - Lecture Notes in Computer Science

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BT - Algorithms and Models for the Web Graph

A2 - Bonata, Anthony

A2 - Chung, Fan Chung

A2 - Pralat, Pawel

PB - Springer

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T2 - 11th International Workshop Algorithms and Models for the Web Graph, WAW 2014

Y2 - 17 December 2014 through 18 December 2014

ER -

Ten Thij M, Ouboter T, Worm D, Litvak N, van den Berg HL, Bhulai S. Modelling of trends in Twitter using retweet graph dynamics. In Bonata A, Chung FC, Pralat P, editors, Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings. Cham, Switzerland: Springer. 2014. p. 132-147. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-13123-8_11

Modelling of trends in Twitter using retweet graph dynamics

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