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 -