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Journal of the Association for Information Science and TechnologyScientific Communication & EducationPeer-reviewed journal article

Framework for assessing the risk to a field from fraudulent research

Chaoqun NiiD, B. Ian HutchinsiD

Abstract

Concerns over research integrity are rising, with increasing attention to potential threats from untrustworthy authors. We established a framework to gauge the potential negative influence of researchers potentially engaged in misconduct. The field of Alzheimers Disease (AD) research has been a focal point of these worries. This study aims to assess the risk posed by questionable studies or individuals potentially engaging in fraudulent science in research by examining citation relationships among papers, taking AD research as an illustrative example. Analysis of citation network structure can elucidate the potential propagation of misinformation arising at the author level. Our analysis revealed that there arent any single authors or papers whose citation connections jeopardize a major portion of the fields literature. This indicates a low probability of single entities undermining the majority of works in this area. However, our findings suggest that attention to research integrity of the most influential scientists is warranted. Some scientists can reach a sizable minority of the literature through citations to their work. Emphasizing oversight of the integrity of these authors is crucial, given their influence on the field. Our study introduces an analytical framework adaptable across various fields and disciplines to evaluate potential risks from fraudulence.

In-depth summary

Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 1 (26 Dec 2024) · CC0 1.0

Citation-network analysis of 112,720 Alzheimer's disease papers finds that no single author or paper reaches most of the field; the top 150 authors average 7.8% literature reach.

Misconduct allegations in Alzheimer's disease (AD) research raised fears that one fraudulent scientist could compromise a whole field, because small-world citation networks let hub papers influence much of the literature. Using open citation data and disambiguated author data, the work measures how far individual papers and authors actually reach and offers a risk-assessment framework that other fields can reuse.

Key results

  1. ShownNo individual author can reach most of the AD literature through direct citations. Most authors reach a tiny share, though the most-cited researchers reach a sizable minority of papers.

    Of 22,941 authors with multiple first- or last-author AD papers, the mean fraction of literature citing an author was 0.3%; under 1% of authors were cited by over 5% of 112,720 papers; the top 150 authors averaged 7.8% reach (92% of papers do not cite them); the single most-cited author reached around 21%. · The risk of citing individual authors; Discussion and Conclusions

  2. ShownInfluence in the AD literature is highly concentrated: a small share of papers and authors receives most citations and produces most output, following approximate power-law distributions.

    10% of AD publications received 57% of AD citations; among 22,941 authors, about 10% contributed 51% of papers, and about 70% of 2,051,603 citations went to 10% of authors; papers per author follow an approximate power law. · Overview of the Alzheimer’s research enterprise; Risk analysis of individual papers; The risk of citing individual authors

  3. ShownMost AD papers cite both a recent work and that work's antecedent (a citation triangle) rather than forming linear chains. The authors take this to mean indirect, second-order citations are unlikely to be the main route by which flawed findings spread.

    65% of papers cited the immediately preceding work plus at least one antecedent, vs 35% citing only the preceding work; triangles were significantly more common than in a randomized network (p < 2.2e-16, n = 2,051,603, Fisher's two-sided exact test). · Risk analysis of individual papers; Discussion and Conclusions; Figure legends

  4. ShownA single questioned paper touches only a small share of the field. The 2006 study under misconduct investigation, though heavily cited, is cited by a minor fraction of the AD corpus, and direct links between papers are rare.

    The investigated paper's 2,300 citations equal about 2% of 112,720 AD papers; average shortest citation path between papers is 4.4; only 0.01% of pathway links (n = 2,051,603) are direct (shortest path=1). · Introduction; Risk analysis of individual papers; Discussion and Conclusions

  5. ShownAn author's citation reach rises closely with their citation volume, so the integrity of the most highly cited researchers matters most for protecting the field.

    Fraction of AD literature citing an author vs number of citations: r = 0.84, p < 2.2e-16, Pearson's correlation test, n = 22,941 authors. · The risk of citing individual authors; Figure legends

Methods, data and reproducibility

Builds an AD knowledge graph from PubMed (MeSH or title query), NIH Open Citation Collection links and PubMed Knowledge Graph author profiles. It then measures shortest citation paths, how often citation triangles occur compared with a randomized network, and the fraction of the corpus citing each author, using Leiden clusters for visualization.

  • Core AD literature from PubMed, tagged with the Alzheimer Disease MeSH heading or with Alzheimer in the title; queried via API in August 2022 · n = 112,720 publications; 193,077 authors
  • NIH Open Citation Collection (iCite) citation graph linking papers in the core AD corpus · n = 2,051,603 within-corpus citations; 4,694,679 total to AD papers
  • Disambiguated author profiles from PubMed Knowledge Graph, filtered to authors with 2+ first- or last-author AD papers · n = 22,941 analyzed (of 75,730 first/last authors)
  • NIH Alzheimer's research projects and funding over time (Figure 1B overview)
Methods & tools
PubMed API, Medical Subject Headings (MeSH), NIH iCite / Open Citation Collection, PubMed Knowledge Graph, Leiden clustering algorithm, igraph R package, Fisher's two-sided exact test, Pearson's correlation test
Software & environment
R with the igraph package for shortest-path analysis; no software versions or container stated.
Reproducibility gaps
How the randomized network was built for the citation-triangle test and which Leiden clustering parameters were used are not described; the corpus is a PubMed snapshot queried in August 2022.
Limitations (6)
  • Only citations inside the AD corpus were analyzed; work in other fields that draws on AD research was not included. · stated by the authors
  • It is untested whether findings from this one field generalize to other disciplines or to the wider scientific enterprise. · stated by the authors
  • The effect of several malicious actors publishing at the same time, whether coordinated or independent, on information flow was not examined. · stated by the authors
  • Reach values are upper bounds: they assume an author's misconduct went undetected in every paper and that every citation reflects causal knowledge transfer rather than an ancillary mention. · stated by the authors
  • The top-150 average of 7.8% is stated against 112,720 papers in the Results but against 2,051,603 citations in the Discussion, so it is unclear whether it is a share of papers or of citation links. · noted in this summary
  • Author reach is measured only through direct (first-order) citations and only for authors with two or more first- or last-author AD papers, so second-order influence and the influence of middle-author-only contributors are not quantified. · noted in this summary

Read this if you study research integrity, bibliometrics or citation networks and want a reusable way to estimate how far one paper's or author's misconduct could spread through a field.

Citations
7
FWCI
5.08
Field percentile
top 4%

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