Trend-based citation count prediction for research articles

Cheng Te Li, Yu Jen Lin, Rui Yan, Mi Yen Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)


This paper aims to predict the future impact, measured by the citation count, of any papers of interest. While existing studies utilized the features related to the paper content or publication information to do Citation Count Prediction (CCP), we propose to leverage the citation count trend of a paper and develop a Trend-based Citation Count Prediction (T-CCP) model. By observing the citation count fluctuation of a paper along with time, we identify five typical citation trends: early burst, middle burst, late burst, multi bursts, and no bursts. T-CCP first performs Citation Trend Classification (CTC) to detect the citation trend of a paper, and then learns the predictive function for each trend to predict the citation count. We investigate two categories of features for CCP, CTC, and T-CCP: the publication features, including author, venue, expertise, social, and reinforcement features, and the early citation behaviors, including citation statistical and structural features. Experiments conducted on the Arnet- Miner citation dataset exhibit promising results that T-CCP outperforms CCP and the proposed features are more effective than conventional ones.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, Proceedings
EditorsTu-Bao Ho, Hiroshi Motoda, Hiroshi Motoda, Ee-Peng Lim, Tru Cao, David Cheung, Zhi-Hua Zhou
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319180373
Publication statusPublished - 2015
Event19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015 - Ho Chi Minh City, Viet Nam
Duration: 2015 May 192015 May 22

Publication series

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


Other19th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2015
Country/TerritoryViet Nam
CityHo Chi Minh City

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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