Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.

@inproceedings{zoller2015publication, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, title = {On Publication Usage in a Social Bookmarking System}, booktitle = {Proceedings of the 2015 ACM Conference on Web Science}, year = {2015}, keywords = {2015, altmetrics, bookmarking, impact, myown, publication, social, usage}, abstract = {Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.} }

%0 = inproceedings %A = Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas %B = Proceedings of the 2015 ACM Conference on Web Science %D = 2015 %T = On Publication Usage in a Social Bookmarking System

@proceedings{jannach2014proceedings,, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social
Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender
Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, series = {CEUR Workshop Proceedings}, publisher = {CEUR-WS.org}, year = {2014}, volume = {1271}, url = {http://ceur-ws.org/Vol-1271}, keywords = {2014, myown, proceedings, recommender, social, workshop} }

%0 = proceedings %B = } %C = %D = 2014 %I = CEUR-WS.org %T = Proceedings of the 6th Workshop on Recommender Systems and the Social
Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender
Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014} %U = http://ceur-ws.org/Vol-1271

Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the

@article{noKey, author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, title = {The social distributional hypothesis: a pragmatic proxy for homophily in online social networks}, journal = {Social Network Analysis and Mining}, publisher = {Springer Vienna}, year = {2014}, volume = {4}, number = {1}, url = {http://dx.doi.org/10.1007/s13278-014-0216-2}, doi = {10.1007/s13278-014-0216-2}, issn = {1869-5450}, keywords = {2014, distributional, hypothesis, myown, pragmatic, proxy, social}, abstract = {Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the } }

%0 = article %A = Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd %D = 2014 %I = Springer Vienna %T = The social distributional hypothesis: a pragmatic proxy for homophily in online social networks %U = http://dx.doi.org/10.1007/s13278-014-0216-2

When users interact with the Web today, they leave sequential digital trails
a massive scale. Examples of such human trails include Web navigation,
quences of online restaurant reviews, or online music play lists.
derstanding the factors that drive the production of these trails can be
eful for e.g., improving underlying network structures, predicting user
icks or enhancing recommendations. In this work, we present a general
proach called HypTrails for comparing a set of hypotheses about human trails
the Web, where hypotheses represent beliefs about transitions between
ates. Our approach utilizes Markov chain models with Bayesian inference. The
in idea is to incorporate hypotheses as informative Dirichlet priors and to
verage the sensitivity of Bayes factors on the prior for comparing hypotheses
th each other. For eliciting Dirichlet priors from hypotheses, we present an
aption of the so-called (trial) roulette method. We demonstrate the general
chanics and applicability of HypTrails by performing experiments with (i)
nthetic trails for which we control the mechanisms that have produced them
d (ii) empirical trails stemming from different domains including website
vigation, business reviews and online music played. Our work expands the
pertoire of methods available for studying human trails on the Web.

@misc{singer2014hyptrails, author = {Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, title = {HypTrails: A Bayesian Approach for Comparing Hypotheses about Human
Trails on the Web}, year = {2014}, note = {cite arxiv:1411.2844}, url = {http://arxiv.org/abs/1411.2844}, keywords = {2014, bayesian, comparing, hypotheses, myown, semantic, social}, abstract = {When users interact with the Web today, they leave sequential digital trailson a massive scale. Examples of such human trails include Web navigation,sequences of online restaurant reviews, or online music play lists.Understanding the factors that drive the production of these trails can beuseful for e.g., improving underlying network structures, predicting userclicks or enhancing recommendations. In this work, we present a generalapproach called HypTrails for comparing a set of hypotheses about human trailson the Web, where hypotheses represent beliefs about transitions betweenstates. Our approach utilizes Markov chain models with Bayesian inference. Themain idea is to incorporate hypotheses as informative Dirichlet priors and toleverage the sensitivity of Bayes factors on the prior for comparing hypotheseswith each other. For eliciting Dirichlet priors from hypotheses, we present anadaption of the so-called (trial) roulette method. We demonstrate the generalmechanics and applicability of HypTrails by performing experiments with (i)synthetic trails for which we control the mechanisms that have produced themand (ii) empirical trails stemming from different domains including websitenavigation, business reviews and online music played. Our work expands therepertoire of methods available for studying human trails on the Web.} }

%0 = misc %A = Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus %B = } %C = %D = 2014 %I = %T = HypTrails: A Bayesian Approach for Comparing Hypotheses about Human
Trails on the Web} %U = http://arxiv.org/abs/1411.2844

@article{strohmaier2014computational, author = {Strohmaier, Markus and Wagner, Claudia}, title = {Computational Social Science for the World Wide Web}, journal = {Intelligent Systems}, publisher = {IEEE }, year = {2014}, pages = {84-88}, keywords = {computational, social} }

%0 = article %A = Strohmaier, Markus and Wagner, Claudia %D = 2014 %I = IEEE %T = Computational Social Science for the World Wide Web