On Publication Usage in a Social Bookmarking System.
In:
Proceedings of the 2015 ACM Conference on Web Science.
2015.
Daniel Zoller, Stephan Doerfel, Robert Jäschke, Gerd Stumme und Andreas Hotho.
[Kurzfassung]
[BibTeX]
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.
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.
CEUR Workshop Proceedings. Band 1271.
CEUR-WS.org, 2014.
Dietmar Jannach, Jill Freyne, Werner Geyer, Ido Guy, Andreas Hotho und Bamshad Mobasher.
[doi]
[BibTeX]
The social distributional hypothesis: a pragmatic proxy for homophily in online social networks.
Social Network Analysis and Mining, 4(1), 2014.
Folke Mitzlaff, Martin Atzmueller, Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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
HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web.
2014. cite arxiv:1411.2844.
Philipp Singer, Denis Helic, Andreas Hotho und Markus Strohmaier.
[doi]
[Kurzfassung]
[BibTeX]
When users interact with the Web today, they leave sequential digital trails on 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 be useful for e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.
Computational Social Science for the World Wide Web.
Intelligent Systems:84-88, 2014.
Markus Strohmaier und Claudia Wagner.
[BibTeX]