Citation concept analysis (CCA) - A new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by two exemplary case studies including classic books by Thomas S. Kuhn and Karl R. Popper
Not on the map. Why?
Abstract (arXiv)
In recent years, the full text of papers are increasingly available electronically which opens up the possibility of quantitatively investigating citation contexts in more detail. In this study, we introduce a new form of citation analysis, which we call citation concept analysis (CCA). CCA is intended to reveal the cognitive impact certain concepts -- published in a document -- have on the citing authors. It counts the number of times the concepts are mentioned (cited) in the citation context of citing publications. We demonstrate the method using three classical examples: (1) The structure of scientific revolutions by Thomas S. Kuhn, (2) The logic of scientific discovery - Logik der Forschung: Zur Erkenntnistheorie der modernen Naturwissenschaft in German -, and (3) Conjectures and refutations: the growth of scientific knowledge by Karl R. Popper. It is not surprising -- as our results show -- that Kuhn's "paradigm" concept has had a significant impact. What is surprising is that it has had such a disproportionately larger impact than Kuhn's other concepts, e.g., "scientific revolution". The paradigm concept accounts for over 80% of the concept-related citations to Kuhn's work, and its impact is resilient across all disciplines and over time. With respect to Popper, "falsification" is the most used concept derived from his books. Falsification, after all, is the cornerstone of Popper's critical rationalism.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 May 2019.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 2; 2025: 3.
9 papers.
- 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
- 2021LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answeringcited by 17
- 2024Dual-path Collaborative Generation Network for Emotional Video Captioningcited by 13
- 2025GS-Cache: A GS-Cache Inference Framework for Large-scale Gaussian Splatting Modelscited by 7
- 2021Investigating Disagreement in the Scientific Literaturecited by 3
- 2022Artificial intelligence in government: Concepts, standards, and a unified frameworkcited by 3
- 2025MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentationcited by 3
- 2023Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskritcited by 1
- 2025Making Sense of Data in the Wild: Data Analysis Automation at Scalecited by 1
- 2025MARL-MambaContour: Unleashing Multi-Agent Deep Reinforcement Learning for Active Contour Optimization in Medical Image Segmentation23 Jun 2025
- 2025GS-Cache: A GS-Cache Inference Framework for Large-scale Gaussian Splatting Models20 Feb 2025
- 2025Making Sense of Data in the Wild: Data Analysis Automation at Scale27 Jan 2025
- 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Survey26 Nov 2024
- 2024Dual-path Collaborative Generation Network for Emotional Video Captioning6 Aug 2024
- 2023Linguistically-Informed Neural Architectures for Lexical, Syntactic and Semantic Tasks in Sanskrit17 Aug 2023
- 2022Artificial intelligence in government: Concepts, standards, and a unified framework31 Oct 2022
- 2021Investigating Disagreement in the Scientific Literature30 Jul 2021
- 2021LPF: A Language-Prior Feedback Objective Function for De-biased Visual Question Answering29 May 2021