Morstatter, F.; ürgen Pfeffer, J.; Liu, H. & Carley, K. M. (2013), 'Is the Sample Good Enough? Comparing Data from Twitters Streaming API with Twitters Firehose', .
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Bullock, B. N.; Jäschke, R. & Hotho, A. (2011), Tagging data as implicit feedback for learning-to-rank, in 'Proceedings of the ACM WebSci'11' .
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Hotho, A.; Ulslev Pedersen, R. & Wurst, M. (2010), 'Ubiquitous Data', Lecture Notes in Computer Science (6202) , 61--74 .
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Song, C.; Qu, Z.; Blumm, N. & Barabási, A.-L. (2010), 'Limits of Predictability in Human Mobility', Science 327 (5968) , 1018-1021 .
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A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual’s trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

2009

Atzmueller, M.; Lemmerich, F.; Krause, B. & Hotho, A. (2009), Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description, in Johannes Fürnkranz Arno Knobbe, ed., 'Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases' .
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Atzmueller, M.; Lemmerich, F.; Krause, B. & Hotho, A. (2009), Who are the Spammers? Understandable Local Patterns for Concept Description, in '7th Conference on Computer Methods and Systems' .
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2008

Krause, B.; Schmitz, C.; Hotho, A. & Stumme, G. (2008), The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems, in 'AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web' , ACM, New York, NY, USA , pp. 61--68 .
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2007

Berendt, B.; Hotho, A.; Mladenic, D. & Semeraro, G., ed. (2007), From Web to Social Web: Discovering and Deploying User and Content Profiles , Vol. 4736 , Springer .
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This book constitutes the refereed proceedings of the Workshop on Web Mining, WebMine 2006, held in Berlin, Germany, September 18th, 2006. Topics included are data mining based on analysis of bloggers and tagging, web mining, XML mining and further techniques of knowledge discovery. The book is especially valuable for those interested in the aspects of the Social Web (Web 2.0) and its inherent dynamic and diversity of user-generated content.

Wurst, M. & Morik, K. (2007), 'Distributed feature extraction in a p2p setting: a case study', Future Gener. Comput. Syst. 23 (1) , 69--75 .
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2006

Baeza-Yates, R.; Calderón-Benavides, L. & González-Caro, C. (2006), 'The Intention Behind Web Queries', String Processing and Information Retrieval , 98--109 .
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The identification of the user’s intention or interest through queries that they submit to a search engine can be very useful to offer them more adequate results. In this work we present a framework for the identification of user’s interest in an automaticway, based on the analysis of query logs. This identification is made from two perspectives, the objectives or goals of auser and the categories in which these aims are situated. A manual classification of the queries was made in order to havea reference point and then we applied supervised and unsupervised learning techniques. The results obtained show that fora considerable amount of cases supervised learning is a good option, however through unsupervised learning we found relationshipsbetween users and behaviors that are not easy to detect just taking the query words. Also, through unsupervised learning weestablished that there are categories that we are not able to determine in contrast with other classes that were not consideredbut naturally appear after the clustering process. This allowed us to establish that the combination of supervised and unsupervisedlearning is a good alternative to find user’s goals. From supervised learning we can identify the user interest given certainestablished goals and categories; on the other hand, with unsupervised learning we can validate the goals and categories used,refine them and select the most appropriate to the user’s needs.

Balakrishnan, H. & Deo, N. (2006), Discovering communities in complex networks., in Ronaldo Menezes, ed., 'ACM Southeast Regional Conference' , ACM, , pp. 280-285 .
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2005

Buitelaar, P.; Cimiano, P. & Magnini, B., ed. (2005), Ontology Learning from Text: Methods, Evaluation and Applications , Vol. 123 , IOS Press .
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2004

Weiss, S. M.; Indurkhya, N. & Zhang, T. (2004), Text Mining. Predictive Methods for Analyzing Unstructured Information , Springer, Berlin .
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2003

Baldi, P.; Frasconi, P. & Smyth, P. (2003), Modeling the Internet and the Web: Probabilistic Methods and Algorithms , Wiley .
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Modeling the Internet and the Web covers the most important aspects of modeling the Web using a modern mathematical and probabilistic treatment. It focuses on the information and application layers, as well as some of the emerging properties of the Internet.  Provides a comprehensive introduction to the modeling of the Internet and the Web at the information level.  Takes a modern approach based on mathematical, probabilistic, and graphical modeling.  Provides an integrated presentation of theory, examples, exercises and applications.  Covers key topics such as text analysis, link analysis, crawling techniques, human behaviour, and commerce on the Web. Interdisciplinary in nature, Modeling the Internet and the Web will be of interest to students and researchers from a variety of disciplines including computer science, machine learning, engineering, statistics, economics, business, and the social sciences.

2002

Dhillon, I. S.; Modha, D. S. & Spangler, W. S. (2002), 'Class visualization of high-dimensional data with applications', Computational Statistics & Data Analysis 41 (1) , 59-90 .
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2001

2000

Masand, B. M. & Spiliopoulou, M., ed. (2000), Web Usage Analysis and User Profiling, International WEBKDD'99
Workshop, San Diego, California, USA, August 15, 1999, Revised
Papers , Vol. 1836 , Springer .
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1999

1996

Fayyad, U. M.; Piatetsky-Shapiro, G. & Smyth, P. (1996), From Data Mining to Knowledge Discovery: An Overview. 'Advances in Knowledge Discovery and Data Mining' , pp. 1-34 .
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1985

Flajolet, P. & Martin, G. N. (1985), 'Probabilistic Counting Algorithms for Data Base Applications', Journal of Computer and System Sciences 31 (2) , 182-209 .
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