Weighting in-text citation location and context meanings for proposing citation quality score metric
Yaniasih Yaniasih, Indra Budi
Performance Measurement and Metrics, Vol. 26, No. 1, pp.34-48

The assessment of citation value necessitates considering the number of citations and the specific characteristics of in-text citations. This article establishes the significance of location and contextual meaning attributes in determining the citation value. It also aims to create a framework for assessing the quality score metric of citations.

The ground truth weights are created using expert opinion through the best-worst method (BWM). Automatic weighting is required for future metric development. Therefore, ground truth weights are compared to five objective multi-criteria decision-making (MCDM) methods: (1) standard deviation, (2) Gini coefficient, (3) entropy, (4) criteria importance through inter-criteria correlation (CRITIC) and (5) method based on criteria removal effects (MEREC). The selected weights are then utilized to create the metric framework.

The highest ground truth weight for the citation attributes is in the method section location (0.2176), positive sentiment (0.4455), role source from the method (0.3537) and function as a comparison (0.2554). The objective method closest to the ground truth is MEREC. Three types of citation quality score (CQS) metrics are developed to assess the significance of citation in a single appearance in a text, within a citing document and across all available data. Notably, a significant correlation exists between proposed metrics and expert judgment.

This article presents a novel citation metric that prioritizes quality factors over traditional metrics, focusing solely on numbers. Comparing various MCDM approaches is a novel approach to scientific measurement research.