Document (#43623)

Author

Huang, S.
Qian, J.
Huang, Y.
Lu, W.
Bu, Y.
Yang, J.
Cheng, Q.

Title

Disclosing the relationship between citation structure and future impact of a publication

Source

Journal of the Association for Information Science and Technology. 73(2022) no.7, S.1025-1042

Year

2022

Abstract

Each section header of an article has its distinct communicative function. Citations from distinct sections may be different regarding citing motivation. In this paper, we grouped section headers with similar functions as a structural function and defined the distribution of citations from structural functions for a paper as its citation structure. We aim to explore the relationship between citation structure and the future impact of a publication and disclose the relative importance among citations from different structural functions. Specifically, we proposed two citation counting methods and a citation life cycle identification method, by which the regression data were built. Subsequently, we employed a ridge regression model to predict the future impact of the paper and analyzed the relative weights of regressors. Based on documents collected from the Association for Computational Linguistics Anthology website, our empirical experiments disclosed that functional structure features improve the prediction accuracy of citation count prediction and that there exist differences among citations from different structural functions. Specifically, at the early stage of citation lifetime, citations from Introduction and Method are particularly important for perceiving future impact of papers, and citations from Result and Conclusion are also vital. However, early accumulation of citations from the Background seems less important.

Content

Vgl.: https://asistdl.onlinelibrary.wiley.com/doi/10.1002/asi.24610.

Theme

Citation indexing

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5. Safder, I.; Ali, M.; Aljohani, N.R.; Nawaz, R.; Hassan, S.-U.: Neural machine translation for in-text citation classification (2023) 0.25

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