How citation distortions create unfounded authority: analysis of a citation network

BMJ 2009; 339 doi: (Published 21 July 2009) Cite this as: BMJ 2009;339:b2680
  1. Steven A Greenberg, associate professor of neurology
  1. 1Children’s Hospital Informatics Program and Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
  1. Correspondence to: S A Greenberg sagreenberg{at}
  • Accepted 17 March 2009


Objective To understand belief in a specific scientific claim by studying the pattern of citations among papers stating it.

Design A complete citation network was constructed from all PubMed indexed English literature papers addressing the belief that β amyloid, a protein accumulated in the brain in Alzheimer’s disease, is produced by and injures skeletal muscle of patients with inclusion body myositis. Social network theory and graph theory were used to analyse this network.

Main outcome measures Citation bias, amplification, and invention, and their effects on determining authority.

Results The network contained 242 papers and 675 citations addressing the belief, with 220 553 citation paths supporting it. Unfounded authority was established by citation bias against papers that refuted or weakened the belief; amplification, the marked expansion of the belief system by papers presenting no data addressing it; and forms of invention such as the conversion of hypothesis into fact through citation alone. Extension of this network into text within grants funded by the National Institutes of Health and obtained through the Freedom of Information Act showed the same phenomena present and sometimes used to justify requests for funding.

Conclusion Citation is both an impartial scholarly method and a powerful form of social communication. Through distortions in its social use that include bias, amplification, and invention, citation can be used to generate information cascades resulting in unfounded authority of claims. Construction and analysis of a claim specific citation network may clarify the nature of a published belief system and expose distorted methods of social citation.


  • I thank Daniel Rockmore (Department of Mathematics, Dartmouth College); Daniel Jonah Goldhagen; Peter Park (Harvard Medical School); and Einer Elhauge (Harvard Law School), for thoughtful discussions. Anthony Amato (Harvard Medical School) carried out validation of extracted text and citations.

  • Funding: SAG is in part supported by National Institutes of Health grants R01NS43471 and R21NS057225, and Muscular Dystrophy Association grant MDA4353. These grants did not contain specific aims directly encompassing this research.

  • Competing interests: SAG had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

  • Ethical approval: Not required.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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