%0 Journal Article %1 zoller2016posted %A Zoller, Daniel %A Doerfel, Stephan %A Jäschke, Robert %A Stumme, Gerd %A Hotho, Andreas %D 2016 %J Journal of Informetrics %K %N 3 %P 732 - 749 %R 10.1016/j.joi.2016.03.005 %T Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy %V 10 %X In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.

@article{zoller2016posted, abstract = {In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.}, added-at = {2016-08-08T01:38:15.000+0200}, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, biburl = {https://www.bibsonomy.org/bibtex/2e9317109a729387f96f25acb6b6a6590/nosebrain}, doi = {10.1016/j.joi.2016.03.005}, interhash = {be7d36039eb3424e7af2a6a94774308a}, intrahash = {e9317109a729387f96f25acb6b6a6590}, journal = {Journal of Informetrics}, keywords = {}, number = 3, pages = {732 - 749}, timestamp = {2016-08-08T01:38:15.000+0200}, title = {Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy}, volume = 10, year = 2016 }