ISSI 2021: Using Citation Bias to Guide Better Sampling of Scientific Literature
Автор: Yuanxi Fu
Загружено: 2021-05-11
Просмотров: 64
Описание: In this presentation, we describe in-progress work on developing a method to help researchers sample diverse research papers without reading all of them. Using insights generated from citation bias studies, we designed two network structure metrics. The first metric, the ratio between real and expected citation count, guides users to papers that may represent marginalized findings. The second metric, the relative evidence coupling strength, guides users to papers that may present a unique view of the field. We describe the preliminary results of testing our methods on a known case of citation bias and outline our future directions.
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