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Date

17.11.2020 12:22:59

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Abstract

This study investigates scholars' citation behaviors from a fine-grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in empirical research, in which a total of 15 interpretable features of scholarly citations were constructed and grouped into three main categories. Each category corresponds to one aspect of the reasons and motivations behind scholars' citation decision-making during academic writing. Using about 500,000 pairs of actual and randomly generated scholarly citations, a series of Random Forest-based classification experiments were conducted to quantitatively evaluate the correlation between each constructed citation feature and citation decisions made by scholars. Our experimental results indicate that citation proximity is the category most relevant to scholars' citation decision-making, followed by citation authority and citation inertia. However, big-name scholars whose h-indexes rank among the top 1% exhibit a unique pattern of citation behaviors-their citation decision-making correlates most closely with citation inertia, with the correlation nearly three times as strong as that of their ordinary counterparts. Hopefully, the empirical findings presented in this paper can bring us closer to characterizing and understanding the complex process of generating scholarly citations in academia.

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Abstract

The quality of scientific publications can be measured by quantitative indices such as the h-index, Source Normalized Impact per Paper, or g-index. However, these measures lack to explain the function or reasons for citations and the context of citations from citing publication to cited publication. We argue that citation context may be considered while calculating the impact of research work. However, mining citation context from unstructured full-text publications is a challenging task. In this paper, we compiled a data set comprising 9,518 citations context. We developed a deep learning-based architecture for citation context classification. Unlike feature-based state-of-the-art models, our proposed focal-loss and class-weight-aware BiLSTM model with pretrained GloVe embedding vectors use citation context as input to outperform them in multiclass citation context classification tasks. Our model improves on the baseline state-of-the-art by achieving an F1 score of 0.80 with an accuracy of 0.81 for citation context classification. Moreover, we delve into the effects of using different word embeddings on the performance of the classification model and draw a comparison between fastText, GloVe, and spaCy pretrained word embeddings.