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Trend detection model

Authors:

Noriaki Kawamae,

Ryuichiro HigashinakaAuthors Info & Claims

WWW '10: Proceedings of the 19th international conference on World wide web

Pages 1129 - 1130

https://doi.org/10.1145/1772690.1772838

Published: 26 April 2010 Publication History

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Abstract

This paper presents a topic model that detects topic distributions over time. Our proposed model, Trend Detection Model (TDM) introduces a latent trend class variable into each document. The trend class has a probability distribution over topics and a continuous distribution over time. Experiments using our data set show that TDM is useful as a generative model in the analysis of the evolution of trends.

References

[1]

D. Blei and J. Lafferty. Dynamic topic models. In ICML, pages 113--120, 2006.

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[2]

X. Wang and A. McCallum. Topics over time: a non-markov continuous-time model of topical trends. In KDD, pages 424--433, 2006.

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Cited By

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Wei WLiu HSun Z(2022)Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction frameworkScientometrics10.1007/s11192-022-04462-y127:8(4315-4333)Online publication date: 18-Jul-2022
https://doi.org/10.1007/s11192-022-04462-y
Lee MSong M(2020)Incorporating citation impact into analysis of research trendsScientometrics10.1007/s11192-020-03508-3Online publication date: 18-May-2020
https://doi.org/10.1007/s11192-020-03508-3
Jadeja PKotecha K(2018)Forecasting the behaviour of Trending Terms in Microblogs2018 IEEE Punecon10.1109/PUNECON.2018.8745388(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/PUNECON.2018.8745388
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Index Terms

Trend detection model
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Time series analysis

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Information

Published In

WWW '10: Proceedings of the 19th international conference on World wide web

April 2010

1407 pages

ISBN:9781605587998

DOI:10.1145/1772690

General Chairs:
Michael Rappa
North Carolina State University, USA
,
Paul Jones
University of North Carolina at Chapel Hill, USA
,
Program Chairs:
Juliana Freire
University of Utah, USA
,
Soumen Chakrabarti
Indian Institute of Technology, India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 April 2010

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WWW '10

WWW '10: The 19th International World Wide Web Conference

April 26 - 30, 2010

North Carolina, Raleigh, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Wei WLiu HSun Z(2022)Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction frameworkScientometrics10.1007/s11192-022-04462-y127:8(4315-4333)Online publication date: 18-Jul-2022
https://doi.org/10.1007/s11192-022-04462-y
Lee MSong M(2020)Incorporating citation impact into analysis of research trendsScientometrics10.1007/s11192-020-03508-3Online publication date: 18-May-2020
https://doi.org/10.1007/s11192-020-03508-3
Jadeja PKotecha K(2018)Forecasting the behaviour of Trending Terms in Microblogs2018 IEEE Punecon10.1109/PUNECON.2018.8745388(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/PUNECON.2018.8745388
Chakraborty TSikdar SGanguly NMukherjee A(2014)Citation interactions among computer science fields: a quantitative route to the rise and fall of scientific researchSocial Network Analysis and Mining10.1007/s13278-014-0187-34:1Online publication date: 12-Apr-2014
https://doi.org/10.1007/s13278-014-0187-3
Weninger T(2014)An exploration of submissions and discussions in social news: mining collective intelligence of RedditSocial Network Analysis and Mining10.1007/s13278-014-0173-94:1Online publication date: 5-Feb-2014
https://doi.org/10.1007/s13278-014-0173-9
Streibel OAlnemr RCantador ICarrero FCortizo JRosso PSchedl MTroyano J(2011)Trend-based and reputation-versed personalized news networkProceedings of the 3rd international workshop on Search and mining user-generated contents10.1145/2065023.2065027(3-10)Online publication date: 28-Oct-2011
https://dl.acm.org/doi/10.1145/2065023.2065027
Hong LYin DGuo JDavison BApte CGhosh JSmyth P(2011)Tracking trendsProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2020408.2020485(484-492)Online publication date: 21-Aug-2011
https://dl.acm.org/doi/10.1145/2020408.2020485
Kawamae NKing INejdl WLi H(2011)Trend analysis modelProceedings of the fourth ACM international conference on Web search and data mining10.1145/1935826.1935880(317-326)Online publication date: 9-Feb-2011
https://dl.acm.org/doi/10.1145/1935826.1935880

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Trend analysis model: trend consists of temporal words, topics, and timestamps

Theme chronicle model: chronicle consists of timestamp and topical words over each theme

Detection and Analysis of Trend Topics for Global Scientific Literature Using Feature Selection Based on Gini-Index

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