Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature

Yezheng Liu; Jicheng Wang; Yang Qian; Yuanchun Jiang; Jianshan Sun; Yidong Chai

doi:10.1109/DSC53577.2021.00050

Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature

Yezheng Liu
, Jicheng Wang
, Yang Qian^*
, Yuanchun Jiang
, Jianshan Sun
, Yidong Chai

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature. © 2021 IEEE.

Original language	English
Title of host publication	Proceedings - 2021 IEEE Sixth International Conference on Data Science in Cyberspace, DSC 2021
Publisher	IEEE
Pages	315-320
Number of pages	6
ISBN (Electronic)	978-1-6654-1815-7
ISBN (Print)	978-1-6654-1816-4
DOIs	https://doi.org/10.1109/DSC53577.2021.00050
Publication status	Published - Oct 2021
Externally published	Yes
Event	6th IEEE International Conference on Data Science in Cyberspace (DSC 2021) - InterContinental Shenzhen, Shenzhen, China Duration: 9 Oct 2021 → 11 Oct 2021 http://www.ieeedsc.org/2021/CallForPapers.html

Publication series

Name	Proceedings - IEEE International Conference on Data Science in Cyberspace, DSC

Conference

Conference	6th IEEE International Conference on Data Science in Cyberspace (DSC 2021)
Place	China
City	Shenzhen
Period	9/10/21 → 11/10/21
Internet address	http://www.ieeedsc.org/2021/CallForPapers.html

Funding

This work is supported by the National Key R&D Program of China (No. 2018YFB1402600), the Major Program of the National Natural Science Foundation of China (91846201), the National Natural Science Foundation of China (72101072, 72171071, 71722010, 91746302, 71872060), the Project funded by China Postdoctoral Science Foundation (2021M690852), Fundamental Research Funds for the Central Universities (JZ2021HGQB0272).

Research Keywords

Dynamic topic model
popularity measurement
topic evolution

Access Output

10.1109/DSC53577.2021.00050

Other Access Options

Check CityUHK Library

Permanent Link

Right click to copy link

Cite this

Liu, Y., Wang, J., Qian, Y., Jiang, Y., Sun, J., & Chai, Y. (2021). Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature. In Proceedings - 2021 IEEE Sixth International Conference on Data Science in Cyberspace, DSC 2021 (pp. 315-320). (Proceedings - IEEE International Conference on Data Science in Cyberspace, DSC). IEEE. https://doi.org/10.1109/DSC53577.2021.00050

@inproceedings{35477ce3b333474bafd254f550f170af,

title = "Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature",

abstract = "With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature. {\textcopyright} 2021 IEEE.",

keywords = "Dynamic topic model, popularity measurement, topic evolution",

author = "Yezheng Liu and Jicheng Wang and Yang Qian and Yuanchun Jiang and Jianshan Sun and Yidong Chai",

year = "2021",

month = oct,

doi = "10.1109/DSC53577.2021.00050",

language = "English",

isbn = "978-1-6654-1816-4",

series = "Proceedings - IEEE International Conference on Data Science in Cyberspace, DSC",

publisher = "IEEE",

pages = "315--320",

booktitle = "Proceedings - 2021 IEEE Sixth International Conference on Data Science in Cyberspace, DSC 2021",

address = "United States",

note = "6th IEEE International Conference on Data Science in Cyberspace (DSC 2021) ; Conference date: 09-10-2021 Through 11-10-2021",

url = "http://www.ieeedsc.org/2021/CallForPapers.html",

}

Liu, Y, Wang, J, Qian, Y, Jiang, Y, Sun, J & Chai, Y 2021, Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature. in Proceedings - 2021 IEEE Sixth International Conference on Data Science in Cyberspace, DSC 2021. Proceedings - IEEE International Conference on Data Science in Cyberspace, DSC, IEEE, pp. 315-320, 6th IEEE International Conference on Data Science in Cyberspace (DSC 2021), Shenzhen, China, 9/10/21. https://doi.org/10.1109/DSC53577.2021.00050

Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature. / Liu, Yezheng; Wang, Jicheng; Qian, Yang et al.
Proceedings - 2021 IEEE Sixth International Conference on Data Science in Cyberspace, DSC 2021. IEEE, 2021. p. 315-320 (Proceedings - IEEE International Conference on Data Science in Cyberspace, DSC).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature

AU - Liu, Yezheng

AU - Wang, Jicheng

AU - Qian, Yang

AU - Jiang, Yuanchun

AU - Sun, Jianshan

AU - Chai, Yidong

PY - 2021/10

Y1 - 2021/10

N2 - With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature. © 2021 IEEE.

AB - With the progress of science and technology, a large number of scientific papers are published every year. Faced with such large data, identifying high-value research and hot research directions has become an interesting and important task. Previous studies focus less on the popularity measurement of research papers. In this paper, we use a dynamic topic model (DTM) for tracking the topic evolution trend and measuring the popularity of research papers based on the topic hotness. To test our model effectiveness, we conduct experiments on 5,964 research papers in the category of management. Our results show that DTM can help to monitor topic evolution and measuring popularity for scientific literature. © 2021 IEEE.

KW - Dynamic topic model

KW - popularity measurement

KW - topic evolution

UR - https://www.scopus.com/pages/publications/85128726048

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85128726048&origin=recordpage

U2 - 10.1109/DSC53577.2021.00050

DO - 10.1109/DSC53577.2021.00050

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-6654-1816-4

T3 - Proceedings - IEEE International Conference on Data Science in Cyberspace, DSC

SP - 315

EP - 320

BT - Proceedings - 2021 IEEE Sixth International Conference on Data Science in Cyberspace, DSC 2021

PB - IEEE

T2 - 6th IEEE International Conference on Data Science in Cyberspace (DSC 2021)

Y2 - 9 October 2021 through 11 October 2021

ER -

Liu Y, Wang J, Qian Y, Jiang Y, Sun J, Chai Y. Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature. In Proceedings - 2021 IEEE Sixth International Conference on Data Science in Cyberspace, DSC 2021. IEEE. 2021. p. 315-320. (Proceedings - IEEE International Conference on Data Science in Cyberspace, DSC). doi: 10.1109/DSC53577.2021.00050

Dynamic Topic Model for Tracking Topic Evolution and Measuring Popularity of Scientific Literature

Abstract

Publication series

Conference

Funding

Research Keywords

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Cite this