Zoller, D.; Doerfel, S.; Jäschke, R.; Stumme, G. & Hotho, A.: On Publication Usage in a Social Bookmarking System. Proceedings of the 2015 ACM Conference on Web Science. 2015

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.

Atzmueller, M.; Becker, M.; Kibanov, M.; Scholz, C.; Doerfel, S.; Hotho, A.; Macek, B.-E.; Mitzlaff, F.; Mueller, J. & Stumme, G.: Ubicon and its Applications for Ubiquitous Social Computing. In: New Review of Hypermedia and Multimedia 20 (2014), Nr. 1, S. 53-77
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The combination of ubiquitous and social computing is an emerging
esearch area which integrates different but complementary methods,
echniques and tools. In this paper, we focus on the Ubicon platform,
ts applications, and a large spectrum of analysis results.

bicon provides an extensible framework for building and hosting applications
argeting both ubiquitous and social environments. We summarize the
rchitecture and exemplify its implementation using four real-world
pplications built on top of Ubicon. In addition, we discuss several
cientific experiments in the context of these applications in order
o give a better picture of the potential of the framework, and discuss
nalysis results using several real-world data sets collected utilizing
bicon.

Doerfel, S.; Zoller, D.; Singer, P.; Niebler, T.; Hotho, A. & Strohmaier, M.: How Social is Social Tagging?. Proceedings of the 23rd International World Wide Web Conference. New York, NY, USA: ACM, 2014WWW 2014

Doerfel, S.; Zoller, D.; Singer, P.; Niebler, T.; Hotho, A. & Strohmaier, M.: Of course we share! Testing Assumptions about Social Tagging Systems. , 2014
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Social tagging systems have established themselves as an important part in
day's web and have attracted the interest from our research community in a
riety of investigations. The overall vision of our community is that simply
rough interactions with the system, i.e., through tagging and sharing of
sources, users would contribute to building useful semantic structures as
ll as resource indexes using uncontrolled vocabulary not only due to the
sy-to-use mechanics. Henceforth, a variety of assumptions about social
gging systems have emerged, yet testing them has been difficult due to the
sence of suitable data. In this work we thoroughly investigate three
ailable assumptions - e.g., is a tagging system really social? - by examining
ve log data gathered from the real-world public social tagging system
bSonomy. Our empirical results indicate that while some of these assumptions
ld to a certain extent, other assumptions need to be reflected and viewed in
very critical light. Our observations have implications for the design of
ture 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 , 2014
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Jannach, D.; Freyne, J.; Geyer, W.; Guy, I.; Hotho, A. & Mobasher, B.: The sixth ACM RecSys workshop on recommender systems and the social
web. Eighth ACM Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, USA - October 06 - 10, 2014. 2014, S. 395
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Mitzlaff, F.; Atzmueller, M.; Hotho, A. & Stumme, G.: The social distributional hypothesis: a pragmatic proxy for homophily in online social networks. In: Social Network Analysis and Mining 4 (2014), Nr. 1,
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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

Singer, P.; Helic, D.; Hotho, A. & Strohmaier, M.: HypTrails: A Bayesian Approach for Comparing Hypotheses about Human
Trails on the Web. , 2014
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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.

Strohmaier, M. & Wagner, C.: Computational Social Science for the World Wide Web. In: Intelligent Systems (2014), S. 84-88

Atzmueller, M.; Chin, A.; Helic, D. & Hotho, A. (Hrsg.): 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. Berlin, Heidelberg: Imprint: Springer, 2013
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Landia, N.; Doerfel, S.; Jäschke, R.; Anand, S. S.; Hotho, A. & Griffiths, N.: Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations. In: cs.IR 1310.1498 (2013),
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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.

Mitzlaff, F.; Atzmueller, M.; Stumme, G. & Hotho, A.: Semantics of User Interaction in Social Media. In: Ghoshal, G.; Poncela-Casasnovas, J. & Tolksdorf, R. (Hrsg.): Complex Networks IV. Heidelberg, Germany: Springer Verlag, 2013 (Studies in Computational Intelligence 476)

Mitzlaff, F.; Atzmueller, M.; Benz, D.; Hotho, A. & Stumme, G.: User-Relatedness and Community Structure in Social Interaction Networks.. In: CoRR abs/1309.3888 (2013),
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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 , 2013
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Sun, X.; Kaur, J.; Milojevic, S.; Flammini, A. & Menczer, F.: Social Dynamics of Science. In: Sci. Rep. 3 (2013),
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Atzmueller, M.; Chin, A.; Helic, D. & Hotho, A. (Hrsg.): Modeling and Mining Ubiquitous Social Media. Heidelberg, Germany: Springer Verlag, 2012 (Lecture Notes in Computer Science 7472)
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Atzmueller, M.; Becker, M.; Doerfel, S.; Kibanov, M.; Hotho, A.; Macek, B.-E.; Mitzlaff, F.; Mueller, J.; Scholz, C. & Stumme, G.: Ubicon: Observing Social and Physical Activities. IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2012, Besançon, France, 20-23 November, 2012. Washington, DC, USA: IEEE, 2012

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.

Mitzlaff, F.; Atzmueller, M.; Stumme, G. & Hotho, A.: On the Semantics of User Interaction in Social Media (Extended Abstract, Resubmission). Proc. LWA 2013 (KDML Special Track). Bamberg, Germany: University of Bamberg, 2011

Ugander, J.; Karrer, B.; Backstrom, L. & Marlow, C.: The Anatomy of the Facebook Social Graph. , 2011
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We study the structure of the social graph of active Facebook users, the
rgest social network ever analyzed. We compute numerous features of the graph
cluding the number of users and friendships, the degree distribution, path
ngths, clustering, and mixing patterns. Our results center around three main
servations. First, we characterize the global structure of the graph,
termining that the social network is nearly fully connected, with 99.91% of
dividuals belonging to a single large connected component, and we confirm the
ix degrees of separation" phenomenon on a global scale. Second, by studying
e average local clustering coefficient and degeneracy of graph neighborhoods,
show that while the Facebook graph as a whole is clearly sparse, the graph
ighborhoods of users contain surprisingly dense structure. Third, we
aracterize the assortativity patterns present in the graph by studying the
sic demographic and network properties of users. We observe clear degree
sortativity and characterize the extent to which "your friends have more
iends than you". Furthermore, we observe a strong effect of age on friendship
eferences as well as a globally modular community structure driven by
tionality, but we do not find any strong gender homophily. We compare our
sults with those from smaller social networks and find mostly, but not
tirely, agreement on common structural network characteristics.

Hotho, A.; Ulslev Pedersen, R. & Wurst, M.: Ubiquitous Data. In: Lecture Notes in Computer Science (2010), Nr. 6202, S. 61-74
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