Identifying Potential Bias in Science Using Citation Network Structures

Time Frame


Total Funding to Date



  • Jodi Schneider

Biased citation benefits authors in the short-term by bolstering grants and papers, making them more easily accepted. However, it can have severe negative consequences for scientific inquiry. The need for a bias detection tool is evident from previous studies on citation bias, but existing work lacks crucial elements needed to scale the underlying approaches. This project will test the hypothesis that citation bias can be detected through a network-structure-only approach. If the hypothesis is not supported, the project will determine how much extra information is needed from the article content (title, abstract, or text). This project is, to our knowledge, the first systematic study of citation bias from a network structure perspective. It will pave the way towards scalable automatic bias detection tools by identifying relevant network structure metrics, and, if necessary, text mining approaches for content extraction.

abstract science concept


  • Yuanxi Fu, PhD student

Funding Agencies

  • Campus Research Board, 2020 – $29,960.00