Predicting the rank of trending topics

Dohyeong Kim; Soyeon Caren Han; Sungyoung Lee; Byeong Ho Kang

doi:10.1007/978-3-319-50127-7_56

Predicting the rank of trending topics

Dohyeong Kim
, Soyeon Caren Han
, Sungyoung Lee
, Byeong Ho Kang

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

Abstract

Trending topics is the most popular term list in the different web services, such as Twitter and Google. The changes in people’s interest in a specific trending topic are reflected in the changes of its popularity rank (up, down, and unchanged). This paper proposes a temporal modelling framework for predicting rank change of trending topics, and delivers the real-time prediction service with only historical rank data. Historical rank data show that almost 70% of trending topics tend to disappear and reappear later. We handled those missing values, using deletion, dummy variable, mean substitution, and expectation maximization. On the other hand, it is necessary to select the optimal window size for the historical rank data. An optimal window size is selected based on the minimum length of topic disappearance in the same topic but with a different context. We examined our approach with four different machine-learning techniques using the twitter trending topics dataset, which is collected for 2 years. As an application, we implemented a trends prediction service, called TrendsForecast, applying our prediction model for Twitter trending topics in 10 different countries.

Original language	English
Title of host publication	AI 2016
Subtitle of host publication	Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings
Editors	Byeong Ho Kang, Quan Bai
Publisher	Springer Verlag
Pages	636-647
Number of pages	12
ISBN (Print)	9783319501260
DOIs	https://doi.org/10.1007/978-3-319-50127-7_56
Publication status	Published - 2016
Event	29th Australasian Joint Conference on Artificial Intelligence, AI 2016 - Hobart, Australia Duration: 5 Dec 2016 → 8 Dec 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9992 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	29th Australasian Joint Conference on Artificial Intelligence, AI 2016
Country/Territory	Australia
City	Hobart
Period	5/12/16 → 8/12/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2016.

Keywords

Temporal prediction
Trending topic
Trends prediction

Access to Document

10.1007/978-3-319-50127-7_56

Cite this

Kim, D., Han, S. C., Lee, S., & Kang, B. H. (2016). Predicting the rank of trending topics. In B. H. Kang, & Q. Bai (Eds.), AI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings (pp. 636-647). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9992 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-50127-7_56

Kim, Dohyeong ; Han, Soyeon Caren ; Lee, Sungyoung et al. / Predicting the rank of trending topics. AI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings. editor / Byeong Ho Kang ; Quan Bai. Springer Verlag, 2016. pp. 636-647 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{40be5ed043604b9a8aaa8b34e4ee417d,

title = "Predicting the rank of trending topics",

abstract = "Trending topics is the most popular term list in the different web services, such as Twitter and Google. The changes in people{\textquoteright}s interest in a specific trending topic are reflected in the changes of its popularity rank (up, down, and unchanged). This paper proposes a temporal modelling framework for predicting rank change of trending topics, and delivers the real-time prediction service with only historical rank data. Historical rank data show that almost 70\% of trending topics tend to disappear and reappear later. We handled those missing values, using deletion, dummy variable, mean substitution, and expectation maximization. On the other hand, it is necessary to select the optimal window size for the historical rank data. An optimal window size is selected based on the minimum length of topic disappearance in the same topic but with a different context. We examined our approach with four different machine-learning techniques using the twitter trending topics dataset, which is collected for 2 years. As an application, we implemented a trends prediction service, called TrendsForecast, applying our prediction model for Twitter trending topics in 10 different countries.",

keywords = "Temporal prediction, Trending topic, Trends prediction",

author = "Dohyeong Kim and Han, \{Soyeon Caren\} and Sungyoung Lee and Kang, \{Byeong Ho\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 29th Australasian Joint Conference on Artificial Intelligence, AI 2016 ; Conference date: 05-12-2016 Through 08-12-2016",

year = "2016",

doi = "10.1007/978-3-319-50127-7\_56",

language = "English",

isbn = "9783319501260",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "636--647",

editor = "Kang, \{Byeong Ho\} and Quan Bai",

booktitle = "AI 2016",

address = "Germany",

}

Kim, D, Han, SC, Lee, S & Kang, BH 2016, Predicting the rank of trending topics. in BH Kang & Q Bai (eds), AI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9992 LNAI, Springer Verlag, pp. 636-647, 29th Australasian Joint Conference on Artificial Intelligence, AI 2016, Hobart, Australia, 5/12/16. https://doi.org/10.1007/978-3-319-50127-7_56

Predicting the rank of trending topics. / Kim, Dohyeong; Han, Soyeon Caren; Lee, Sungyoung et al.
AI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings. ed. / Byeong Ho Kang; Quan Bai. Springer Verlag, 2016. p. 636-647 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9992 LNAI).

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

TY - GEN

T1 - Predicting the rank of trending topics

AU - Kim, Dohyeong

AU - Han, Soyeon Caren

AU - Lee, Sungyoung

AU - Kang, Byeong Ho

N1 - Publisher Copyright: © Springer International Publishing AG 2016.

PY - 2016

Y1 - 2016

N2 - Trending topics is the most popular term list in the different web services, such as Twitter and Google. The changes in people’s interest in a specific trending topic are reflected in the changes of its popularity rank (up, down, and unchanged). This paper proposes a temporal modelling framework for predicting rank change of trending topics, and delivers the real-time prediction service with only historical rank data. Historical rank data show that almost 70% of trending topics tend to disappear and reappear later. We handled those missing values, using deletion, dummy variable, mean substitution, and expectation maximization. On the other hand, it is necessary to select the optimal window size for the historical rank data. An optimal window size is selected based on the minimum length of topic disappearance in the same topic but with a different context. We examined our approach with four different machine-learning techniques using the twitter trending topics dataset, which is collected for 2 years. As an application, we implemented a trends prediction service, called TrendsForecast, applying our prediction model for Twitter trending topics in 10 different countries.

AB - Trending topics is the most popular term list in the different web services, such as Twitter and Google. The changes in people’s interest in a specific trending topic are reflected in the changes of its popularity rank (up, down, and unchanged). This paper proposes a temporal modelling framework for predicting rank change of trending topics, and delivers the real-time prediction service with only historical rank data. Historical rank data show that almost 70% of trending topics tend to disappear and reappear later. We handled those missing values, using deletion, dummy variable, mean substitution, and expectation maximization. On the other hand, it is necessary to select the optimal window size for the historical rank data. An optimal window size is selected based on the minimum length of topic disappearance in the same topic but with a different context. We examined our approach with four different machine-learning techniques using the twitter trending topics dataset, which is collected for 2 years. As an application, we implemented a trends prediction service, called TrendsForecast, applying our prediction model for Twitter trending topics in 10 different countries.

KW - Temporal prediction

KW - Trending topic

KW - Trends prediction

UR - https://www.scopus.com/pages/publications/85007165429

U2 - 10.1007/978-3-319-50127-7_56

DO - 10.1007/978-3-319-50127-7_56

M3 - Conference contribution

AN - SCOPUS:85007165429

SN - 9783319501260

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

SP - 636

EP - 647

BT - AI 2016

A2 - Kang, Byeong Ho

A2 - Bai, Quan

PB - Springer Verlag

T2 - 29th Australasian Joint Conference on Artificial Intelligence, AI 2016

Y2 - 5 December 2016 through 8 December 2016

ER -

Kim D, Han SC, Lee S, Kang BH. Predicting the rank of trending topics. In Kang BH, Bai Q, editors, AI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Proceedings. Springer Verlag. 2016. p. 636-647. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-50127-7_56

Predicting the rank of trending topics

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this