@article{sun2013social, author = {Sun, Xiaoling and Kaur, Jasleen and Milojevic, Stasa and Flammini, Alessandro and Menczer, Filippo}, comment = {10.1038/srep01069}, interhash = {5cd31392e997555d78596f962044f84b}, intrahash = {ed28353b082f3ccbd23ea85ea9d7c8e5}, journal = {Sci. Rep.}, month = jan, publisher = {Macmillan Publishers Limited. All rights reserved}, title = {Social Dynamics of Science}, url = {http://dx.doi.org/10.1038/srep01069}, volume = 3, year = 2013 } @article{Haustein2011446, abstract = {Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in STM and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content.}, author = {Haustein, Stefanie and Siebenlist, Tobias}, doi = {10.1016/j.joi.2011.04.002}, interhash = {13fe59aae3d6ef95b529ffe00ede4126}, intrahash = {60170943fb293bcb54754710ec9dced1}, issn = {1751-1577}, journal = {Journal of Informetrics}, number = 3, pages = {446 - 457}, title = {Applying social bookmarking data to evaluate journal usage}, url = {http://www.sciencedirect.com/science/article/pii/S1751157711000393}, volume = 5, year = 2011 } @inproceedings{transybil2009, author = {Tran, D.N. and Min, B. and Li, J. and Subramanian, L.}, interhash = {34d39d14be357a65eefa8207a3fb5856}, intrahash = {40c3dea03e3e4c561db6bc4b34c6f3da}, organization = {Citeseer}, title = {Sybil-resilient online content rating}, url = {http://scholar.google.com/scholar.bib?q=info:YVSgj4tFvzEJ:scholar.google.com/&output=citation&hl=de&as_sdt=0&scfhb=1&ct=citation&cd=0}, year = 2009 } @book{noauthororeditor2011privacy, editor = {Trepte, Sabine and Reinecke, Leonard}, interhash = {0c1381abf25ce1766bf35b1d3b72d87b}, intrahash = {6b40774e3fee58c844c9e059e77691df}, isbn = {9783642215209 3642215203}, pages = {--}, publisher = {Springer-Verlag New York Inc}, refid = {731921793}, title = {Privacy Online Perspectives on Privacy and Self-disclosure in the Social Web.}, url = {http://www.worldcat.org/search?qt=worldcat_org_all&q=9783642215209}, year = 2011 } @article{citeulike:8506476, abstract = {{Social tagging systems pose new challenges to developers of recommender systems. As observed by recent research, traditional implementations of classic recommender approaches, such as collaborative filtering, are not working well in this new context. To address these challenges, a number of research groups worldwide work on adapting these approaches to the specific nature of social tagging systems. In joining this stream of research, we have developed and evaluated two enhancements of user-based collaborative filtering algorithms to provide recommendations of articles on Cite ULike, a social tagging service for scientific articles. The result obtained after two phases of evaluation suggests that both enhancements are beneficial. Incorporating the number of raters into the algorithms, as we do in our NwCF approach, leads to an improvement of precision, while tag-based BM25 similarity measure, an alternative to Pearson correlation for calculating the similarity between users and their neighbors, increases the coverage of the recommendation process.}}, address = {Los Alamitos, CA, USA}, author = {Santander, Denis P. and Brusilovsky, Peter}, citeulike-article-id = {8506476}, citeulike-linkout-0 = {http://doi.ieeecomputersociety.org/10.1109/WI-IAT.2010.261}, citeulike-linkout-1 = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, doi = {10.1109/WI-IAT.2010.261}, interhash = {dd320da969151c01cf270976c0803274}, intrahash = {2c8764f2fe11ef1ae43fc0a5b51301ae}, isbn = {978-0-7695-4191-4}, journal = {Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on}, pages = {136--142}, posted-at = {2011-01-05 00:19:36}, priority = {0}, publisher = {IEEE Computer Society}, title = {{Improving Collaborative Filtering in Social Tagging Systems for the Recommendation of Scientific Articles}}, url = {http://dx.doi.org/10.1109/WI-IAT.2010.261}, volume = 1, year = 2010 } @book{noauthororeditoryahoo, abstract = {The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information. Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining }, author = {Tang‌, Lei and Liu‌, Huan}, doi = {10.2200/S00298ED1V01Y201009DMK003}, interhash = {717f8b976eec1dc934a3b84675456f25}, intrahash = {c4e1fa6bf2d52a237e5557640d87c970}, title = {Community Detection and Mining in Social Media}, url = {http://www.morganclaypool.com/doi/abs/10.2200/S00298ED1V01Y201009DMK003}, year = 2010 } @misc{Kitsak2010, abstract = { Networks portray a multitude of interactions through which people meet, ideas are spread, and infectious diseases propagate within a society. Identifying the most efficient "spreaders" in a network is an important step to optimize the use of available resources and ensure the more efficient spread of information. Here we show that, in contrast to common belief, the most influential spreaders in a social network do not correspond to the best connected people or to the most central people (high betweenness centrality). Instead, we find: (i) The most efficient spreaders are those located within the core of the network as identified by the k-shell decomposition analysis. (ii) When multiple spreaders are considered simultaneously, the distance between them becomes the crucial parameter that determines the extend of the spreading. Furthermore, we find that-- in the case of infections that do not confer immunity on recovered individuals-- the infection persists in the high k-shell layers of the network under conditions where hubs may not be able to preserve the infection. Our analysis provides a plausible route for an optimal design of efficient dissemination strategies. }, author = {Kitsak, Maksim and Gallos, Lazaros K. and Havlin, Shlomo and Liljeros, Fredrik and Muchnik, Lev and Stanley, H. Eugene and Makse, Hernan A.}, interhash = {9545e268e6074cf2edc21693e7bb1b04}, intrahash = {18a1220e45e38620051a0c9b854d1a28}, note = {cite arxiv:1001.5285 Comment: 31 pages, 12 figures}, title = {Identifying influential spreaders in complex networks}, url = {http://arxiv.org/abs/1001.5285}, year = 2010 } @article{1304546, abstract = {Social bookmarking services have recently gained popularity among Web users. Whereas numerous studies provide a historical account of tagging systems, the authors use their analysis of a domain-specific social bookmarking service called CiteULike to reflect on two metrics for evaluating tagging behavior: tag growth and tag reuse. They examine the relationship between these two metrics and articulate design implications for enhancing social bookmarking services. The authors also briefly reflect on their own work on developing a social bookmarking service for CiteSeer, an online scholarly digital library for computer science.}, address = {Piscataway, NJ, USA}, author = {Farooq, Umer and Song, Yang and Carroll, John M. and Giles, C. Lee}, doi = {http://dx.doi.org/10.1109/MIC.2007.135}, interhash = {13183e8fc4cbe0944a819afa2d9ff4eb}, intrahash = {5785e8a8064b3d346f8c198c3c860bf6}, issn = {1089-7801}, journal = {IEEE Internet Computing}, number = 6, pages = {29--35}, publisher = {IEEE Educational Activities Department}, title = {Social Bookmarking for Scholarly Digital Libraries}, url = {http://portal.acm.org/citation.cfm?id=1304546&coll=Portal&dl=GUIDE&CFID=46454031&CFTOKEN=27530397}, volume = 11, year = 2007 } @inproceedings{taggingsem08, abstract = {At present tagging is experimenting a great diffusion as the most adopted way to collaboratively classify resources over the Web. In this paper, after a detailed analysis of the attempts made to improve the organization and structure of tagging systems as well as the usefulness of this kind of social data, we propose and evaluate the Tag Disambiguation Algorithm, mining del.icio.us data. It allows to easily semantify the tags of the users of a tagging service: it automatically finds out for each tag the related concept of Wikipedia in order to describe Web resources through senses. On the basis of a set of evaluation tests, we analyze all the advantages of our sense-based way of tagging, proposing new methods to keep the set of users tags more consistent or to classify the tagged resources on the basis of Wikipedia categories, YAGO classes or Wordnet synsets. We discuss also how our semanitified social tagging data are strongly linked to DBPedia and the datasets of the Linked Data community. }, author = {Tesconi, Maurizio and Ronzano, Francesco and Marchetti, Andrea and Minutoli, Salvatore}, crossref = {CEUR-WS.org/Vol-405}, interhash = {0c1c96b41a0af8512c20a7d41504640f}, intrahash = {348a962fe13e0b605ffc53d592464c24}, title = {Semantify del.icio.us: Automatically Turn your Tags into Senses}, url = {http://CEUR-WS.org/Vol-405/paper8.pdf}, year = 2008 } @article{juliaonline, abstract = {Zusammenfassung  Online Social Networks wie Xing.com oder Facebook.com gehören zu den am stärksten wachsenden Diensten im Internet. Im Jahr 2008 nutzten geschätzte 580 Mio. Menschen weltweit diese Angebote. Entsprechend schnell haben sich Online Social Networksinnerhalb weniger Jahre von einem Nischenphänomen zu einem weltweiten Medium der IT-gestützten Kommunikation entwickelt. Insbesondereaufgrund stark wachsender Mitgliederzahlen entfalten Online Social Networks eine erhebliche gesellschaftliche und wirtschaftlicheBedeutung. Vor diesem Hintergrund ist es Ziel dieses Beitrags, Begriff und Eigenschaften, Entstehung und Entwicklung sowieNutzenpotenziale und Herausforderungen von Online Social Networks näher zu untersuchen.}, author = {Heidemann, Julia}, interhash = {d535135f1f523873830c4e19f16fdf61}, intrahash = {602e2e19ec9de91f4f992cd1486bc0df}, journal = {Informatik-Spektrum}, pages = {--}, title = {Online Social Networks – Ein sozialer und technischer Überblick}, url = {http://dx.doi.org/10.1007/s00287-009-0367-0}, year = 2009 } @inproceedings{heymann2008social, abstract = {In this paper, we look at the "social tag prediction" problem. Given a set of objects, and a set of tags applied to those objects by users, can we predict whether a given tag could/should be applied to a particular object? We investigated this question using one of the largest crawls of the social bookmarking system del.icio.us gathered to date. For URLs in del.icio.us, we predicted tags based on page text, anchor text, surrounding hosts, and other tags applied to the URL. We found an entropy-based metric which captures the generality of a particular tag and informs an analysis of how well that tag can be predicted. We also found that tag-based association rules can produce very high-precision predictions as well as giving deeper understanding into the relationships between tags. Our results have implications for both the study of tagging systems as potential information retrieval tools, and for the design of such systems.}, address = {New York, NY, USA}, author = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector}, booktitle = {SIGIR '08: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval}, doi = {http://doi.acm.org/10.1145/1390334.1390425}, interhash = {bb9455c80cc9bd8cf95c951a1318dabc}, intrahash = {0e6023e192f539fe4fce9894b1fbca5a}, isbn = {978-1-60558-164-4}, location = {Singapore, Singapore}, pages = {531--538}, publisher = {ACM}, title = {Social tag prediction}, url = {http://portal.acm.org/citation.cfm?id=1390334.1390425}, year = 2008 } @incollection{citeulike:3149792, abstract = {The motivation behind many Information Retrieval systems is to identify and present relevant information to people given their current goals and needs. Learning about user preferences and access patterns recent technologies make it possible to model user information needs and adapt services to meet these needs. In previous work we have presented ASSIST, a general-purpose platform which incorporates various types of social support into existing information access systems and reported on our deployment experience in a highly goal driven environment (ACM Digital Library). In this work we present our experiences in applying ASSIST to a domain where goals are less focused and where casual exploration is more dominant; YouTube. We present a general study of YouTube access patterns and detail how the ASSIST architecture affected the access patterns of users in this domain.}, author = {Coyle, Maurice and Freyne, Jill and Brusilovsky, Peter and Smyth, Barry}, citeulike-article-id = {3149792}, doi = {http://dx.doi.org/10.1007/978-3-540-70987-9\_12}, interhash = {487512d7286ca43ca9b96ee4a0efc198}, intrahash = {f75eb556b19abd7b399f2f27ae49cb1c}, journal = {Adaptive Hypermedia and Adaptive Web-Based Systems}, pages = {93--102}, posted-at = {2008-10-13 00:16:23}, priority = {2}, title = {Social Information Access for the Rest of Us: An Exploration of Social YouTube}, url = {http://www.springerlink.com/content/6h410u3w4836v866/}, year = 2008 } @inproceedings{1458098, address = {New York, NY, USA}, author = {Song, Yang and Zhang, Lu and Giles, C. Lee}, booktitle = {CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge mining}, doi = {http://doi.acm.org/10.1145/1458082.1458098}, interhash = {5c03bc1e658b6d44f053944418bdaec3}, intrahash = {d330a3537b4a14fbd40661424ec8e465}, isbn = {978-1-59593-991-3}, location = {Napa Valley, California, USA}, pages = {93--102}, publisher = {ACM}, title = {A sparse gaussian processes classification framework for fast tag suggestions}, url = {http://portal.acm.org/citation.cfm?id=1458098}, year = 2008 } @inproceedings{1316677, address = {New York, NY, USA}, author = {Farooq, Umer and Kannampallil, Thomas G. and Song, Yang and Ganoe, Craig H. and Carroll, John M. and Giles, Lee}, booktitle = {GROUP '07: Proceedings of the 2007 international ACM conference on Conference on supporting group work}, doi = {http://doi.acm.org/10.1145/1316624.1316677}, interhash = {66928ca91bf0d777b848fe6f7a55de20}, intrahash = {5d0b61727d81aed019ba4297090108ca}, isbn = {978-1-59593-845-9}, location = {Sanibel Island, Florida, USA}, pages = {351--360}, publisher = {ACM}, title = {Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics}, url = {http://portal.acm.org/citation.cfm?id=1316677&coll=Portal&dl=GUIDE&CFID=9767993&CFTOKEN=86305662}, year = 2007 } @inproceedings{Noll/2007/search, abstract = {In this paper, we present a new approach to web search personalization based on user collaboration and sharing of information about web documents. The proposed personalization technique separates data collection and user profiling from the information system whose contents and indexed documents are being searched for, i.e. the search engines, and uses social bookmarking and tagging to re-rank web search results. It is independent of the search engine being used, so users are free to choose the one they prefer, even if their favorite search engine does not natively support personalization. We show how to design and implement such a system in practice and investigate its feasibility and usefulness with large sets of real-word data and a user study.}, address = {Berlin, Heidelberg}, author = {Noll, Michael and Meinel, Christoph}, booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea}, crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings}, editor = {Aberer, Karl and Choi, Key-Sun and Noy, Natasha and Allemang, Dean and Lee, Kyung-Il and Nixon, Lyndon J B and Golbeck, Jennifer and Mika, Peter and Maynard, Diana and Schreiber, Guus and Cudré-Mauroux, Philippe}, interhash = {8c1f9db1455effa2cdf949c0191a31d2}, intrahash = {52943a6298169f5a552bffbbee352937}, month = {November}, pages = {365--378}, publisher = {Springer Verlag}, series = {LNCS}, title = {Web search personalization via social bookmarking and tagging}, url = {http://iswc2007.semanticweb.org/papers/365.pdf}, volume = 4825, year = 2007 } @article{Lerman:2007p3955, abstract = {The rise of the social media sites, such as blogs, wikis, Digg and Flickr among others, underscores the transformation of the Web to a participatory medium in which users are collaboratively creating, evaluating and distributing information. The innovations introduced by social media has lead to a new paradigm for interacting with information, what we call 'social information processing'. In this paper, we study how social news aggregator Digg exploits social information processing to solve the problems of document recommendation and rating. First, we show, by tracking stories over time, that social networks play an important role in document recommendation. The second contribution of this paper consists of two mathematical models. The first model describes how collaborative rating and promotion of stories emerges from the independent decisions made by many users. The second model describes how a user's influence, the number of promoted stories and the user's social network, changes in time. We find qualitative agreement between predictions of the model and user data gathered from Digg.}, author = {Lerman, Kristina}, date-added = {2008-02-07 01:06:26 +0100}, date-modified = {2008-02-07 02:25:10 +0100}, interhash = {aec2fc56edd502101e68e669b50ee17f}, intrahash = {7a080f640fa62fc81e73b9fab1e7447c}, journal = {arXiv}, local-url = {file://localhost/Users/bertilhatt/Documents/Papers/Lerman/2007/Lerman%202007%20arXiv.pdf}, month = Jan, pmid = {11330701288966819101related:HY3tKMq8Pp0J}, rating = {0}, read = {Yes}, title = {Social Information Processing in Social News Aggregation}, uri = {papers://C3B117CD-23C4-4854-9426-AC96AFB113DA/Paper/p3955}, url = {http://arxiv.org/abs/cs.CY/0703087}, year = 2007 } @inproceedings{Byde2007, abstract = {This short paper describes a novel technique for generating personalized tag recommendations for users of social book- marking sites such as del.icio.us. Existing techniques recom- mend tags on the basis of their popularity among the group of all users; on the basis of recent use; or on the basis of simple heuristics to extract keywords from the url being tagged. Our method is designed to complement these approaches, and is based on recommending tags from urls that are similar to the one in question, according to two distinct similarity metrics, whose principal utility covers complementary cases.}, author = {Byde, Andrew and Wan, Hui and Cayzer, Steve}, booktitle = {Proceedings of the International Conference on Weblogs and Social Media}, interhash = {38aaca7e5b9c508a5901f4109dabaa69}, intrahash = {157846898c1c2a65c265a913ebac115a}, month = {March}, priority = {5}, title = {Personalized Tag Recommendations via Tagging and Content-based Similarity Metrics}, url = {http://www.icwsm.org/papers/paper47.html}, year = 2007 } @article{keyhere, abstract = {Personalized recommendation is used to conquer the information overload problem, and collaborative filtering recommendation (CF) is one of the most successful recommendation techniques to date. However, CF becomes less effective when users have multiple interests, because users have similar taste in one aspect may behave quite different in other aspects. Information got from social bookmarking websites not only tells what a user likes, but also why he or she likes it. This paper proposes a division algorithm and a CubeSVD algorithm to analysis this information, distill the interrelations between different users’ various interests, and make better personalized recommendation based on them. Experiment reveals the superiority of our method over traditional CF methods. ER -}, author = {Xu, Yanfei and Zhang, Liang and Liu, Wei}, interhash = {edf999afa5a0ff81e53b0c859b466659}, intrahash = {5fbd24f07fe8784b516e69b0eb3192f3}, journal = {Frontiers of WWW Research and Development - APWeb 2006}, pages = {733--738}, title = {Cubic Analysis of Social Bookmarking for Personalized Recommendation}, url = {http://dx.doi.org/10.1007/11610113_66}, year = 2006 } @inproceedings{jin:07:eswc, author = {Jin, YingZi and Matsuo, Yutaka and Ishizuka, Mitsuru}, booktitle = {Proceedings of the European Semantic Web Conference, ESWC2007}, editor = {Franconi, Enrico and Kifer, Michael and May, Wolfgang}, interhash = {cbac61a5d054dbb3d53798e85466464d}, intrahash = {69e2126f99c29bda2c747bf6aceaaa8f}, month = {July}, publisher = {Springer-Verlag}, series = {Lecture Notes in Computer Science}, title = {{Extracting Social Networks among Various Entities on the Web}}, url = {http://www.eswc2007.org/pdf/eswc07-jin.pdf}, volume = 4519, year = 2007 } @misc{text2006Mehler, author = {Mehler, A.}, booktitle = {Proceedings of the EACL 2006 Workshop on New Text-Wikis and blogs and other dynamic text sources}, date = {(2006):April 3-7}, editor = {Jussi, Karlgren}, interhash = {7b7bd4573d9d121dccb5a489084e06d7}, intrahash = {be8f323d6ff541a4f6355f8dce8b5790}, location = {Trento, Italy}, pages = {1-8}, title = {Text Linkage in the Wiki Medium-A comparative study}, url = {http://www.sics.se/jussi/newtext/working_notes/01_mehler.pdf}, year = 2006 }