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| Pattern Mining Based on Attribution Analysis and Its Empirical Study |
| Cui Yunxue, Wang Xianwen, Wang Yongzhen |
| WISE Lab, Institute of Science of Science and S&T Management, Dalian University of Technology, Dalian 116024 |
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Abstract The citation patterns of academic literature exhibit various citation motives, which restrict the in-depth understanding of researchers’ citation behavior. To address this issue, this study took attribution analysis as its research perspective and collected 500,000 citation relationships from PubMed Central as the research sample to reveal the composition of reasons behind citation patterns and quantitatively explain them. First, 12 citation reasons were selected as features to characterize the citation relationships from the academic and non-academic motives of citation. Second, a decision forest algorithm was used to conduct classification experiments on 500,000 real citation relationships and an equal number of paired virtual citation relationships based on the constructed features. Finally, the experimental results were attributed through the SHapley Additive exPlanations (SHAP) explanatory framework, evaluating the influence and mode of action in the 12 citation reasons in citation decisions. The empirical results indicate that the roles played by different citation reasons in deciding whether to cite an article vary considerably. Specifically, factors such as the relevance of a topic, similarity of the research context, and academic level of the cited author play a major role in the citation decision, while other factors such as journal influence and topic frontiers play a minor role. In addition, different citation reasons function in different ways on citation decisions, and the relationship between the change in the value of their features and degree of influence on citation decisions can be summarized into four types: S-curve, logarithmic growth, dichotomous, and random fluctuation.
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Received: 01 April 2022
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