Compare methods

	Mendeley Readership Analysis	Citation Distribution Modeling (Lognormal/Tsallis)
Field	Bibliometrics	Bibliometrics
Family	Process / pipeline	Process / pipeline
Year of origin	2014	2008
Originator	Ehsan Mohammadi & Mike Thelwall	Filippo Radicchi, Santo Fortunato & Claudio Castellano
Type	Altmetric pipeline using reference-manager readership counts	Statistical-modeling pipeline for the shape of citation distributions
Seminal source	Mohammadi, E., & Thelwall, M. (2014). Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows. Journal of the Association for Information Science and Technology, 65(8), 1627-1638.	Radicchi, F., Fortunato, S., & Castellano, C. (2008). Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences, 105(45), 17268-17272.
Aliases	Reader Count Analysis, Mendeley Reader Metrics, Readership Altmetrics, Reference Manager Bookmarking Analysis	Citation Distribution Analysis, Universality of Citation Distributions, Relative Citation Indicator, Discounted Cumulative Citation Modeling
Related	3	3
Summary	Mendeley readership analysis uses the number of users who have saved an article to their personal library in the Mendeley reference manager as an indicator of scholarly attention. Ehsan Mohammadi and Mike Thelwall showed in 2014 that these reader counts have broad coverage, correlate moderately with later citations, and, because saving precedes citing, become available much earlier than citation data. Mendeley also exposes coarse demographic categories for its readers, such as students, researchers, and professionals, allowing analysis of who is engaging with research, including non-citing audiences in the social sciences and humanities. As one of the most studied altmetric sources, Mendeley readership offers an early and relatively well-covered signal that complements citations, while raising distinct questions about what saving a paper actually means.	Citation distribution modeling studies the statistical shape of how citations are spread across papers and uses that shape to compare impact fairly across very different fields. The pivotal result, from Filippo Radicchi, Santo Fortunato, and Claudio Castellano in 2008, is that although raw citation distributions differ enormously between disciplines, they collapse onto a single universal curve once each paper's citations are divided by the average for its field and year. This relative indicator turns an unfair comparison, a mathematics paper against a biomedicine paper, into a fair one by asking how each paper performs relative to its own field's baseline. The universal curve is well described by a lognormal form, and related work has used Tsallis or stretched-exponential and discounted-cumulative formulations, giving scientometrics a principled statistical foundation for normalization rather than ad hoc field adjustments.