Journal of Data and Information Science ›› 2020, Vol. 5 ›› Issue (4): 19-34.doi: 10.2478/jdis-2020-0034

• Research Paper • Previous Articles     Next Articles

A Micro Perspective of Research Dynamics Through “Citations of Citations” Topic Analysis

Xiaoli Chen1,2,(), Tao Han1,2   

  1. 1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-02-08 Revised:2020-05-20 Accepted:2020-06-11 Online:2020-09-20 Published:2020-11-20
  • Contact: Xiaoli Chen E-mail:chenxl@mail.las.ac.cn

Abstract:

Purpose: Research dynamics have long been a research interest. It is a macro perspective tool for discovering temporal research trends of a certain discipline or subject. A micro perspective of research dynamics, however, concerning a single researcher or a highly cited paper in terms of their citations and “citations of citations” (forward chaining) remains unexplored.

Design/methodology/approach: In this paper, we use a cross-collection topic model to reveal the research dynamics of topic disappearance topic inheritance, and topic innovation in each generation of forward chaining.

Findings: For highly cited work, scientific influence exists in indirect citations. Topic modeling can reveal how long this influence exists in forward chaining, as well as its influence.

Research limitations: This paper measures scientific influence and indirect scientific influence only if the relevant words or phrases are borrowed or used in direct or indirect citations. Paraphrasing or semantically similar concept may be neglected in this research.

Practical implications: This paper demonstrates that a scientific influence exists in indirect citations through its analysis of forward chaining. This can serve as an inspiration on how to adequately evaluate research influence.

Originality: The main contributions of this paper are the following three aspects. First, besides research dynamics of topic inheritance and topic innovation, we model topic disappearance by using a cross-collection topic model. Second, we explore the length and character of the research impact through “citations of citations” content analysis. Finally, we analyze the research dynamics of artificial intelligence researcher Geoffrey Hinton’s publications and the topic dynamics of forward chaining.

Key words: Research dynamics, Forward chaining, Topic model, Scientific influence, Citations content analysis