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Understanding the Impact of Early Citers on Long-Term Scientific Impact
This paper explores an interesting new dimension to the challenging problem
of predicting long-term scientific impact (LTSI) usually measured by the number
of citations accumulated by a paper in the long-term. It is well known that
early citations (within 1-2 years after publication) acquired by a paper
positively affects its LTSI. However, there is no work that investigates if the
set of authors who bring in these early citations to a paper also affect its
LTSI. In this paper, we demonstrate for the first time, the impact of these
authors whom we call early citers (EC) on the LTSI of a paper. Note that this
study of the complex dynamics of EC introduces a brand new paradigm in citation
behavior analysis. Using a massive computer science bibliographic dataset we
identify two distinct categories of EC - we call those authors who have high
overall publication/citation count in the dataset as influential and the rest
of the authors as non-influential. We investigate three characteristic
properties of EC and present an extensive analysis of how each category
correlates with LTSI in terms of these properties. In contrast to popular
perception, we find that influential EC negatively affects LTSI possibly owing
to attention stealing. To motivate this, we present several representative
examples from the dataset. A closer inspection of the collaboration network
reveals that this stealing effect is more profound if an EC is nearer to the
authors of the paper being investigated. As an intuitive use case, we show that
incorporating EC properties in the state-of-the-art supervised citation
prediction models leads to high performance margins. At the closing, we present
an online portal to visualize EC statistics along with the prediction results
for a given query paper
Tracing scientific influence
Scientometrics is the field of quantitative studies of scholarly activity. It
has been used for systematic studies of the fundamentals of scholarly practice
as well as for evaluation purposes. Although advocated from the very beginning
the use of scientometrics as an additional method for science history is still
under explored. In this paper we show how a scientometric analysis can be used
to shed light on the reception history of certain outstanding scholars. As a
case, we look into citation patterns of a specific paper by the American
sociologist Robert K. Merton.Comment: 25 pages LaTe
Positional Effects on Citation and Readership in arXiv
arXiv.org mediates contact with the literature for entire scholarly
communities, both through provision of archival access and through daily email
and web announcements of new materials, potentially many screenlengths long. We
confirm and extend a surprising correlation between article position in these
initial announcements, ordered by submission time, and later citation impact,
due primarily to intentional "self-promotion" on the part of authors. A pure
"visibility" effect was also present: the subset of articles accidentally in
early positions fared measurably better in the long-term citation record than
those lower down. Astrophysics articles announced in position 1, for example,
overall received a median number of citations 83\% higher, while those there
accidentally had a 44\% visibility boost. For two large subcommunities of
theoretical high energy physics, hep-th and hep-ph articles announced in
position 1 had median numbers of citations 50\% and 100\% larger than for
positions 5--15, and the subsets there accidentally had visibility boosts of
38\% and 71\%.
We also consider the positional effects on early readership. The median
numbers of early full text downloads for astro-ph, hep-th, and hep-ph articles
announced in position 1 were 82\%, 61\%, and 58\% higher than for lower
positions, respectively, and those there accidentally had medians
visibility-boosted by 53\%, 44\%, and 46\%. Finally, we correlate a variety of
readership features with long-term citations, using machine learning methods,
thereby extending previous results on the predictive power of early readership
in a broader context. We conclude with some observations on impact metrics and
dangers of recommender mechanisms.Comment: 28 pages, to appear in JASIS
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