Time-sensitive topic derivation in Twitter

Robertus Nugroho; Weiliang Zhao; Jian Yang; Cecile Paris; Surya Nepal; Yan Mei

doi:10.1007/978-3-319-26190-4_10

Time-sensitive topic derivation in Twitter

Robertus Nugroho^*, Weiliang Zhao, Jian Yang, Cecile Paris, Surya Nepal, Yan Mei

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

5 Citations (Scopus)

Abstract

Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/ government communication. These activities have used mostly Twitter content to derive topics. More recently, tweet interactions have also been considered, leading to better topics. Given the dynamic aspect of Twitter, we hypothesize that temporal features could further improve topic derivation on a Twitter collection. In this paper, we first perform experiments to characterize the temporal features of the interactions in Twitter. We then propose a time-sensitive topic derivation method. The proposed method incorporates temporal features when it clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2015
Subtitle of host publication	16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1
Editors	Jianyong Wang, Wojciech Cellary, Dingding Wang, Hua Wang, Shu-Ching Chen, Tao Li, Yanchun Zhang
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	138-152
Number of pages	15
ISBN (Electronic)	9783319261904
ISBN (Print)	9783319261898
DOIs	https://doi.org/10.1007/978-3-319-26190-4_10
Publication status	Published - 2015
Event	16th International Conference on Web Information Systems Engineering, WISE 2015 - Miami, United States Duration: 1 Nov 2015 → 3 Nov 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer International Publishing
Volume	9418
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	16th International Conference on Web Information Systems Engineering, WISE 2015
Country	United States
City	Miami
Period	1/11/15 → 3/11/15

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Cite this

Nugroho, R., Zhao, W., Yang, J., Paris, C., Nepal, S., & Mei, Y. (2015). Time-sensitive topic derivation in Twitter. In J. Wang, W. Cellary, D. Wang, H. Wang, S-C. Chen, T. Li, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1 (pp. 138-152). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9418). Cham: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-26190-4_10

Nugroho, Robertus ; Zhao, Weiliang ; Yang, Jian ; Paris, Cecile ; Nepal, Surya ; Mei, Yan. / Time-sensitive topic derivation in Twitter. Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1. editor / Jianyong Wang ; Wojciech Cellary ; Dingding Wang ; Hua Wang ; Shu-Ching Chen ; Tao Li ; Yanchun Zhang. Cham : Springer, Springer Nature, 2015. pp. 138-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3c7bce2979974728b1c76ccb32d3d743,

title = "Time-sensitive topic derivation in Twitter",

abstract = "Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/ government communication. These activities have used mostly Twitter content to derive topics. More recently, tweet interactions have also been considered, leading to better topics. Given the dynamic aspect of Twitter, we hypothesize that temporal features could further improve topic derivation on a Twitter collection. In this paper, we first perform experiments to characterize the temporal features of the interactions in Twitter. We then propose a time-sensitive topic derivation method. The proposed method incorporates temporal features when it clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.",

author = "Robertus Nugroho and Weiliang Zhao and Jian Yang and Cecile Paris and Surya Nepal and Yan Mei",

year = "2015",

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editor = "Jianyong Wang and Wojciech Cellary and Dingding Wang and Hua Wang and Shu-Ching Chen and Tao Li and Yanchun Zhang",

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Nugroho, R, Zhao, W , Yang, J , Paris, C, Nepal, S & Mei, Y 2015, Time-sensitive topic derivation in Twitter. in J Wang, W Cellary, D Wang, H Wang, S-C Chen, T Li & Y Zhang (eds), Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9418, Springer, Springer Nature, Cham, pp. 138-152, 16th International Conference on Web Information Systems Engineering, WISE 2015, Miami, United States, 1/11/15. https://doi.org/10.1007/978-3-319-26190-4_10

Time-sensitive topic derivation in Twitter. / Nugroho, Robertus; Zhao, Weiliang ; Yang, Jian ; Paris, Cecile; Nepal, Surya; Mei, Yan.

Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1. ed. / Jianyong Wang; Wojciech Cellary; Dingding Wang; Hua Wang; Shu-Ching Chen; Tao Li; Yanchun Zhang. Cham : Springer, Springer Nature, 2015. p. 138-152 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9418).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution

TY - GEN

T1 - Time-sensitive topic derivation in Twitter

AU - Nugroho, Robertus

AU - Zhao, Weiliang

AU - Yang, Jian

AU - Paris, Cecile

AU - Nepal, Surya

AU - Mei, Yan

PY - 2015

Y1 - 2015

N2 - Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/ government communication. These activities have used mostly Twitter content to derive topics. More recently, tweet interactions have also been considered, leading to better topics. Given the dynamic aspect of Twitter, we hypothesize that temporal features could further improve topic derivation on a Twitter collection. In this paper, we first perform experiments to characterize the temporal features of the interactions in Twitter. We then propose a time-sensitive topic derivation method. The proposed method incorporates temporal features when it clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.

AB - Much research has been concerned with deriving topics from Twitter and applying the outcomes in a variety of real life applications such as emergency management, business advertisements and corporate/ government communication. These activities have used mostly Twitter content to derive topics. More recently, tweet interactions have also been considered, leading to better topics. Given the dynamic aspect of Twitter, we hypothesize that temporal features could further improve topic derivation on a Twitter collection. In this paper, we first perform experiments to characterize the temporal features of the interactions in Twitter. We then propose a time-sensitive topic derivation method. The proposed method incorporates temporal features when it clusters the tweets and identifies the representative terms for each topic. Our experimental results show that the inclusion of temporal features into topic derivation results in a significant improvement for both topic clustering accuracy and topic coherence comparing to existing baseline methods.

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UR - http://purl.org/au-research/grants/arc/LP120200231

U2 - 10.1007/978-3-319-26190-4_10

DO - 10.1007/978-3-319-26190-4_10

M3 - Conference proceeding contribution

SN - 9783319261898

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 138

EP - 152

BT - Web Information Systems Engineering – WISE 2015

A2 - Wang, Jianyong

A2 - Cellary, Wojciech

A2 - Wang, Dingding

A2 - Wang, Hua

A2 - Chen, Shu-Ching

A2 - Li, Tao

A2 - Zhang, Yanchun

PB - Springer, Springer Nature

CY - Cham

ER -

Nugroho R, Zhao W , Yang J , Paris C, Nepal S, Mei Y. Time-sensitive topic derivation in Twitter. In Wang J, Cellary W, Wang D, Wang H, Chen S-C, Li T, Zhang Y, editors, Web Information Systems Engineering – WISE 2015: 16th International Conference, Miami, FL, USA, November 1-3, 2015, Proceedings, Part 1. Cham: Springer, Springer Nature. 2015. p. 138-152. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-26190-4_10