| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Bar-Ilan, J., Haustein, S., Peters, I., Priem, J., Shema, H. & Terliesner, J. | Beyond citations: Scholars' visibility on the social Web | 2012 | Vol. 1Proceedings of 17th International Conference on Science and Technology Indicators, Montréal: Science-Metrix and OST, pp. 98-109 |
inproceedings | URL |
| Abstract: Traditionally, scholarly impact and visibility have been measured by counting blications and citations in the scholarly literature. However, increasingly holars are also visible on the Web, establishing presences in a growing riety of social ecosystems. But how wide and established is this presence, d how do measures of social Web impact relate to their more traditional unterparts? To answer this, we sampled 57 presenters from the 2010 Leiden STI nference, gathering publication and citations counts as well as data from the esenters' Web "footprints." We found Web presence widespread and diverse: 84% scholars had homepages, 70% were on LinkedIn, 23% had public Google Scholar ofiles, and 16% were on Twitter. For sampled scholars' publications, social ference manager bookmarks were compared to Scopus and Web of Science tations; we found that Mendeley covers more than 80% of sampled articles, and at Mendeley bookmarks are significantly correlated (r=.45) to Scopus citation unts. |
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BibTeX:
@inproceedings{barilan2012beyond,
author = {Bar-Ilan, Judit and Haustein, Stefanie and Peters, Isabella and Priem, Jason and Shema, Hadas and Terliesner, Jens},
title = {Beyond citations: Scholars' visibility on the social Web},
booktitle = {Proceedings of 17th International Conference on Science and Technology Indicators, Montréal: Science-Metrix and OST},
year = {2012},
volume = {1},
pages = {98-109},
url = {http://2012.sticonference.org/Proceedings/vol1/Bar-Ilan_Beyond_98.pdf}
}
|
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| Bar-Ilan, J., Haustein, S., Peters, I., Priem, J., Shema, H. & Terliesner, J. | Beyond citations: Scholars' visibility on the social Web | 2012 | article | URL | |
| Abstract: Traditionally, scholarly impact and visibility have been measured by counting blications and citations in the scholarly literature. However, increasingly holars are also visible on the Web, establishing presences in a growing riety of social ecosystems. But how wide and established is this presence, d how do measures of social Web impact relate to their more traditional unterparts? To answer this, we sampled 57 presenters from the 2010 Leiden STI nference, gathering publication and citations counts as well as data from the esenters' Web "footprints." We found Web presence widespread and diverse: 84% scholars had homepages, 70% were on LinkedIn, 23% had public Google Scholar ofiles, and 16% were on Twitter. For sampled scholars' publications, social ference manager bookmarks were compared to Scopus and Web of Science tations; we found that Mendeley covers more than 80% of sampled articles, and at Mendeley bookmarks are significantly correlated (r=.45) to Scopus citation unts. |
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BibTeX:
@article{barilan2012beyond,
author = {Bar-Ilan, Judit and Haustein, Stefanie and Peters, Isabella and Priem, Jason and Shema, Hadas and Terliesner, Jens},
title = {Beyond citations: Scholars' visibility on the social Web},
year = {2012},
url = {http://dblp.uni-trier.de/db/journals/corr/corr1205.html#abs-1205-5611}
}
|
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| Peters, I., Haustein, S. & Terliesner, J. | Crowdsourcing in Article Evaluation | 2011 | ACM WebSci'11, pp. 1-4 | inproceedings | URL |
| Abstract: Qualitative journal evaluation makes use of cumulated content scriptions of single articles. These can either be represented by thor-generated keywords, professionally indexed subject adings, automatically extracted terms or by reader-generated gs as used in social bookmarking systems. It is assumed that rticularly the users? view on article content differs significantly om the authors? or indexers? perspectives. To verify this sumption, title and abstract terms, author keywords, Inspec bject headings, KeyWords PlusTM and tags are compared by lculating the overlap between the respective datasets. Our proach includes extensive term preprocessing (i.e. stemming, elling unifications) to gain a homogeneous term collection. en term overlap is calculated for every single document of the taset, similarity values are low. Thus, the presented study nfirms the assumption, that the different types of keywords ch reflect a different perspective of the articles? contents and at tags (cumulated across articles) can be used in journal aluation to represent a reader-specific view on published ntent. |
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BibTeX:
@inproceedings{peters2011crowdsourcing,
author = {Peters, Isabella and Haustein, Stefanie and Terliesner, Jens},
title = {Crowdsourcing in Article Evaluation},
booktitle = {ACM WebSci'11},
year = {2011},
pages = {1--4},
note = {WebSci Conference 2011},
url = {http://journal.webscience.org/487/}
}
|
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