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 and Andreas Hotho.
[abstract]
[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.
Ubicon and its Applications for Ubiquitous Social Computing.
New Review of Hypermedia and Multimedia, 20(1):53-77, 2014.
Martin Atzmueller, Martin Becker, Mark Kibanov, Christoph Scholz, Stephan Doerfel, Andreas Hotho, Bjoern-Elmar Macek, Folke Mitzlaff, Juergen Mueller and Gerd Stumme.
[doi]
[abstract]
[BibTeX]
The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.
How Social is Social Tagging?.
In:
Proceedings of the 23rd International World Wide Web Conference, series WWW 2014.
ACM, New York, NY, USA, 2014.
Stephan Doerfel, Daniel Zoller, Philipp Singer, Thomas Niebler, Andreas Hotho and Markus Strohmaier.
[BibTeX]
Of course we share! Testing Assumptions about Social Tagging Systems.
2014. cite arxiv:1401.0629.
Stephan Doerfel, Daniel Zoller, Philipp Singer, Thomas Niebler, Andreas Hotho and Markus Strohmaier.
[doi]
[abstract]
[BibTeX]
Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.
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. volume 1271.
CEUR-WS.org, 2014.
Dietmar Jannach, Jill Freyne, Werner Geyer, Ido Guy, Andreas Hotho and Bamshad Mobasher.
[doi]
[BibTeX]
The sixth ACM RecSys workshop on recommender systems and the social web.
In:
Eighth ACM Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014, pages 395.
2014.
Dietmar Jannach, Jill Freyne, Werner Geyer, Ido Guy, Andreas Hotho and 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 and Gerd Stumme.
[doi]
[abstract]
[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 and Markus Strohmaier.
[doi]
[abstract]
[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 and Claudia Wagner.
[BibTeX]
Ubiquitous Social Media Analysis Third International Workshops, MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers.
2013.
[doi]
[BibTeX]
Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations.
cs.IR, 1310.1498, 2013.
Nikolas Landia, Stephan Doerfel, Robert Jäschke, Sarabjot Singh Anand, Andreas Hotho and Nathan Griffiths.
[doi]
[abstract]
[BibTeX]
The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
Semantics of User Interaction in Social Media.
In:
G. Ghoshal, J. Poncela-Casasnovas and R. Tolksdorf, editors,
Complex Networks IV.
Springer Verlag, Heidelberg, Germany, 2013.
Folke Mitzlaff, Martin Atzmueller, Gerd Stumme and Andreas Hotho.
[BibTeX]
User-Relatedness and Community Structure in Social Interaction Networks..
CoRR, abs/1309.3888, 2013.
Folke Mitzlaff, Martin Atzmueller, Dominik Benz, Andreas Hotho and Gerd Stumme.
[doi]
[BibTeX]
Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013..
CEUR Workshop Proceedings. volume 1066.
CEUR-WS.org, 2013.
Bamshad Mobasher, Dietmar Jannach, Werner Geyer, Jill Freyne, Andreas Hotho, Sarabjot Singh Anand and Ido Guy.
[doi]
[BibTeX]
Social Dynamics of Science.
Sci. Rep., 3, 2013.
Xiaoling Sun, Jasleen Kaur, Stasa Milojevic, Alessandro Flammini and Filippo Menczer.
[doi]
[BibTeX]
Modeling and Mining Ubiquitous Social Media.
2012.
[doi]
[BibTeX]
Ubicon: Observing Social and Physical Activities.
In:
IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012.
IEEE, Washington, DC, USA, 2012.
Martin Atzmueller, Martin Becker, Stephan Doerfel, Mark Kibanov, Andreas Hotho, Björn-Elmar Macek, Folke Mitzlaff, Juergen Mueller, Christoph Scholz and Gerd Stumme.
[abstract]
[BibTeX]
The connection of ubiquitous and social computing is an emerging research area which is combining two prominent areas of computer science. In this paper, we tackle this topic from different angles: We describe data mining methods for ubiquitous and social data, specifically focusing on physical and social activities, and provide exemplary analysis results. Furthermore, we give an overview on the Ubicon platform which provides a framework for the creation and hosting of ubiquitous and social applications for diverse tasks and projects. Ubicon features the collection and analysis of both physical and social activities of users for enabling inter-connected applications in ubiquitous and social contexts. We summarize three real-world systems built on top of Ubicon, and exemplarily discuss the according mining and analysis aspects.
On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission).
In:
Proc. LWA 2013 (KDML Special Track).
University of Bamberg, Bamberg, Germany, 2011.
Folke Mitzlaff, Martin Atzmueller, Gerd Stumme and Andreas Hotho.
[BibTeX]
The Anatomy of the Facebook Social Graph.
2011. cite arxiv:1111.4503Comment: 17 pages, 9 figures, 1 table.
Johan Ugander, Brian Karrer, Lars Backstrom and Cameron Marlow.
[doi]
[abstract]
[BibTeX]
We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the "six degrees of separation" phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which "your friends have more friends than you". Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics.
Ubiquitous Data.
Lecture Notes in Computer Science(6202):61-74, 2010.
Andreas Hotho, Rasmus Ulslev Pedersen and Michael Wurst.
[doi]
[BibTeX]