The Google Scholar Experiment: how to index false papers and manipulate bibliometric indicators
arXiv:1309.2413 · doi:10.1002/asi.23056
Abstract
Google Scholar has been well received by the research community. Its promises of free, universal and easy access to scientific literature as well as the perception that it covers better than other traditional multidisciplinary databases the areas of the Social Sciences and the Humanities have contributed to the quick expansion of Google Scholar Citations and Google Scholar Metrics: two new bibliometric products that offer citation data at the individual level and at journal level. In this paper we show the results of a experiment undertaken to analyze Google Scholar's capacity to detect citation counting manipulation. For this, six documents were uploaded to an institutional web domain authored by a false researcher and referencing all the publications of the members of the EC3 research group at the University of Granada. The detection of Google Scholar of these papers outburst the citations included in the Google Scholar Citations profiles of the authors. We discuss the effects of such outburst and how it could affect the future development of such products not only at individual level but also at journal level, especially if Google Scholar persists with its lack of transparency.
This paper has been accepted for publication in the Journal of the American Society for Information Science and Technology. It is based on a previous working paper available at arXiv:1212.0638.pdf. arXiv admin note: substantial text overlap with arXiv:1212.0638
References in corpus (2)
Cited by in corpus (21)
- Google Scholar, Web of Science, and Scopus: a systematic comparison of citations in 252 subject categories
- The Natural Selection of Bad Science
- Dimensions: A Competitor to Scopus and the Web of Science?
- A review of the characteristics of 108 author-level bibliometric indicators
- Coverage of highly-cited documents in Google Scholar, Web of Science, and Scopus: a multidisciplinary comparison
- Can we use Google Scholar to identify highly-cited documents?
- Author-level metrics in the new academic profile platforms: The online behaviour of the Bibliometrics community
- Empirical Evidences in Citation-Based Search Engines: Is Microsoft Academic Search dead?
- Microsoft Academic Automatic Document Searches: Accuracy for Journal Articles and Suitability for Citation Analysis
- A novel method for depicting academic disciplines through Google Scholar Citations: The case of Bibliometrics
- H-Index Manipulation by Merging Articles: Models, Theory, and Experiments
- Sneaked references: Cooked reference metadata inflate citation counts
- Google Scholar Metrics 2014: a low cost bibliometric tool
- A review of the literature on citation impact indicators
- h-Index Manipulation by Undoing Merges
- Analyzing the disciplinary focus of universities: Can rankings be a one-size-fits-all?
- A new methodology for comparing Google Scholar and Scopus
- Professional and Citizen Bibliometrics: Complementarities and ambivalences in the development and use of indicators
- Hunting for supernovae articles in the universe of scientometrics
- Learning to Drive on the Wrong Side of the Road: How American Computing Came to Rely on Conferences for Primary Publication
- Faculty citation measures are highly correlated with peer assessment of computer science doctoral programs