TY - CONF AU - Bar-Ilan, Judit AU - Haustein, Stefanie AU - Peters, Isabella AU - Priem, Jason AU - Shema, Hadas AU - Terliesner, Jens A2 - Archambault, Éric A2 - Gingras, Yves A2 - Larivière, Vincent T1 - Beyond citations: Scholars' visibility on the social Web T2 - Proceedings of 17th International Conference on Science and Technology Indicators, Montréal: Science-Metrix and OST PB - C1 - PY - 2012/ CY - VL - 1 IS - SP - 98 EP - 109 UR - http://2012.sticonference.org/Proceedings/vol1/Bar-Ilan_Beyond_98.pdf DO - KW - visibility KW - social KW - correlation KW - citations KW - authors KW - web L1 - SN - N1 - Beyond citations: Scholars' visibility on the social Web N1 - AB - Traditionally, scholarly impact and visibility have been measured by counting
publications and citations in the scholarly literature. However, increasingly
scholars are also visible on the Web, establishing presences in a growing
variety of social ecosystems. But how wide and established is this presence,
and how do measures of social Web impact relate to their more traditional
counterparts? To answer this, we sampled 57 presenters from the 2010 Leiden STI
Conference, gathering publication and citations counts as well as data from the
presenters' Web "footprints." We found Web presence widespread and diverse: 84%
of scholars had homepages, 70% were on LinkedIn, 23% had public Google Scholar
profiles, and 16% were on Twitter. For sampled scholars' publications, social
reference manager bookmarks were compared to Scopus and Web of Science
citations; we found that Mendeley covers more than 80% of sampled articles, and
that Mendeley bookmarks are significantly correlated (r=.45) to Scopus citation
counts. ER - TY - JOUR AU - Bar-Ilan, Judit AU - Haustein, Stefanie AU - Peters, Isabella AU - Priem, Jason AU - Shema, Hadas AU - Terliesner, Jens T1 - Beyond citations: Scholars' visibility on the social Web JO - PY - 2012/ VL - IS - SP - EP - UR - http://dblp.uni-trier.de/db/journals/corr/corr1205.html#abs-1205-5611 DO - KW - scholars KW - visibility KW - social KW - citations KW - web L1 - SN - N1 - N1 - AB - Traditionally, scholarly impact and visibility have been measured by counting
publications and citations in the scholarly literature. However, increasingly
scholars are also visible on the Web, establishing presences in a growing
variety of social ecosystems. But how wide and established is this presence,
and how do measures of social Web impact relate to their more traditional
counterparts? To answer this, we sampled 57 presenters from the 2010 Leiden STI
Conference, gathering publication and citations counts as well as data from the
presenters' Web "footprints." We found Web presence widespread and diverse: 84%
of scholars had homepages, 70% were on LinkedIn, 23% had public Google Scholar
profiles, and 16% were on Twitter. For sampled scholars' publications, social
reference manager bookmarks were compared to Scopus and Web of Science
citations; we found that Mendeley covers more than 80% of sampled articles, and
that Mendeley bookmarks are significantly correlated (r=.45) to Scopus citation
counts. ER - TY - CONF AU - Peters, Isabella AU - Haustein, Stefanie AU - Terliesner, Jens A2 - T1 - Crowdsourcing in Article Evaluation T2 - ACM WebSci'11 PB - C1 - PY - 2011/06 CY - VL - IS - SP - 1 EP - 4 UR - http://journal.webscience.org/487/ DO - KW - tags KW - keywords KW - crowd KW - citation L1 - SN - N1 - Crowdsourcing in Article Evaluation - Web Science Repository N1 - AB - Qualitative journal evaluation makes use of cumulated content
descriptions of single articles. These can either be represented by
author-generated keywords, professionally indexed subject
headings, automatically extracted terms or by reader-generated
tags as used in social bookmarking systems. It is assumed that
particularly the users? view on article content differs significantly
from the authors? or indexers? perspectives. To verify this
assumption, title and abstract terms, author keywords, Inspec
subject headings, KeyWords PlusTM and tags are compared by
calculating the overlap between the respective datasets. Our
approach includes extensive term preprocessing (i.e. stemming,
spelling unifications) to gain a homogeneous term collection.
When term overlap is calculated for every single document of the
dataset, similarity values are low. Thus, the presented study
confirms the assumption, that the different types of keywords
each reflect a different perspective of the articles? contents and
that tags (cumulated across articles) can be used in journal
evaluation to represent a reader-specific view on published
content. ER -