Code behind my website at jasonpriem.org
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What can social media tell us about an article's impact?
License: Other
Code behind my website at jasonpriem.org
Just noticed, on arXiv manuscript, Figure 13 says cluster A is 21% and B is 20%. In Table 4 it says the opposite.
in the arXiv manuscript, the caption in Table 5 has a new sentence break. Should be all one sentence, as:
Confusion Matrix (cross classifications) of k-means clusters reported in rows vs. rule-based classifications reported in columns
https://github.com/jasonpriem/plos_altmetrics_study/wiki/data_sources
noticed a shift in the citations for the section on F1000, e.g.
"Post-publication peer review service Faculty of 1000 (F1000) has attracted several studies of its article rankings, with inconsistent findings: [54] reports that the reviewers failed to spot many significant articles, while [55] emphasize that reviewers uncovered many “hidden gems.”"
looks like these should be refs 56 and 57, no? I didn't look closely at other citations, and this might be just a consequence of the loading to arxiv rather than anything in your manuscript...
Missing exponent, should read:
The bars that drop below the 10^0 (== 1) line represent the value of zero.
Maybe is fine in the PLoS draft... looks like 100 in altmetrics draft.
Slippery slope to include more citations, I know, but figured this one was worth a quick consideration:
http://arxiv.org/abs/1205.4328
The weakening relationship between the Impact Factor and papers' citations in the digital age
George A. Lozano, Vincent Lariviere, Yves Gingras
(Submitted on 19 May 2012)
Historically, papers have been physically bound to the journal in which they were published but in the electronic age papers are available individually, no longer tied to their respective journals. Hence, papers now can be read and cited based on their own merits, independently of the journal's physical availability, reputation, or Impact Factor. We compare the strength of the relationship between journals' Impact Factors and the actual citations received by their respective papers from 1902 to 2009. Throughout most of the 20th century, papers' citation rates were increasingly linked to their respective journals' Impact Factors. However, since 1990, the advent of the digital age, the strength of the relation between Impact Factors and paper citations has been decreasing. This decrease began sooner in physics, a field that was quicker to make the transition into the electronic domain. Furthermore, since 1990, the proportion of highly cited papers coming from highly cited journals has been decreasing, and accordingly, the proportion of highly cited papers not coming from highly cited journals has also been increasing. Should this pattern continue, it might bring an end to the use of the Impact Factor as a way to evaluate the quality of journals, papers and researchers.
I want to report decay functions for the events that we have timestamps for. A plot of log number of tweets against log days latency is nearly a straight line, but darned if I know where to go from there, other than I think we want the nlm() function. I looked at http://www.itc.nl/~rossiter/teach/R/R_CurveFit.pdf, but it seems beyond my ken.
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