### Abstract

Original language | Undefined |
---|---|

Title of host publication | Proceedings 11th International Workshop Algorithms and Models for the Web Graph, WAW 2014 |

Editors | Anthony Bonata, Fan Chung Chung, Pawel Pralat |

Place of Publication | Switzerland |

Publisher | Springer International Publishing |

Pages | 132-147 |

Number of pages | 16 |

ISBN (Print) | 978-3-319-13123-8 |

DOIs | |

State | Published - 17 Dec 2014 |

### Publication series

Name | Lecture Notes in Computer Science |
---|---|

Publisher | Springer International Publishing |

Number | 8882 |

Volume | 2014 |

### Fingerprint

### Keywords

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

### Cite this

*Proceedings 11th International Workshop Algorithms and Models for the Web Graph, WAW 2014*(pp. 132-147). (Lecture Notes in Computer Science; Vol. 2014, No. 8882). Switzerland: Springer International Publishing. DOI: 10.1007/978-3-319-13123-8_11

}

*Proceedings 11th International Workshop Algorithms and Models for the Web Graph, WAW 2014.*Lecture Notes in Computer Science, no. 8882, vol. 2014, Springer International Publishing, Switzerland, pp. 132-147. DOI: 10.1007/978-3-319-13123-8_11

**Modelling of trends in Twitter using retweet graph dynamics.** / Ten Thij, Marijn; Ouboter, Tanneke; Worm, Daniël; Litvak, Nelli; van den Berg, Hans Leo; Bhulai, Sandjai.

Research output: Scientific - peer-review › Conference contribution

TY - CHAP

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

KW - Twitter

KW - Random graph model

KW - METIS-309800

KW - Retweet graph

KW - IR-93646

KW - Graph dynamics

U2 - 10.1007/978-3-319-13123-8_11

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

M3 - Conference contribution

SN - 978-3-319-13123-8

T3 - Lecture Notes in Computer Science

SP - 132

EP - 147

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

PB - Springer International Publishing

ER -