@electronic{hinterholzer2013social,
  address = {Berlin, Heidelberg},
  author = {Hinterholzer, Thomas and Jooss, Mario},
  format = {ebook},
  interhash = {c93cb9676cb08bbc9253951a8003d86f},
  intrahash = {ed962a9fe0f2a5647fb2c6f8ec2bb5f7},
  isbn = {3642379524 (Sekundärausgabe)},
  primaryauthor = {Hinterholzer, Thomas},
  publisher = {Imprint: Springer Gabler},
  shorttitle = {Social Media Marketing und -Management im Tourismus},
  subtitle = {[Elektronische Ressource] / von Thomas Hinterholzer, Mario Jooss},
  title = {Social Media Marketing und -Management im Tourismus},
  titlestatement = {von Thomas Hinterholzer, Mario Jooss},
  uniqueid = {HEB332097048},
  url = {http://scans.hebis.de/HEBCGI/show.pl?33209704_toc.html},
  year = 2013
}

@article{grimmer2013text,
  author = {Grimmer, Justin and Stewart, Brandon M},
  interhash = {eb68e01ef4168a398d79f408042fe529},
  intrahash = {76001ebc726700bef81886d2e285b7cf},
  journal = {Political Analysis},
  pages = {mps028},
  publisher = {SPM-PMSAPSA},
  title = {Text as data: The promise and pitfalls of automatic content analysis methods for political texts},
  year = 2013
}

@article{SSQU:SSQU478,
  abstract = {Objective.  This study is an effort to produce a more systematic, empirically-based, historical-comparative understanding of media bias than generally is found in previous works.Methods.  The research employs a quantitative measure of ideological bias in a formal content analysis of the United States' two largest circulation news magazines, Time and Newsweek. Findings are compared with the results of an identical examination of two of the nation's leading partisan journals, the conservative National Review and the liberal Progressive.Results.  Bias scores reveal stark differences between the mainstream and the partisan news magazines' coverage of four issue areas: crime, the environment, gender, and poverty.Conclusion.  Data provide little support for those claiming significant media bias in either ideological direction.},
  author = {Covert, Tawnya J. Adkins and Wasburn, Philo C.},
  doi = {10.1111/j.1540-6237.2007.00478.x},
  interhash = {9276222b3b8684048db1e42c3a9f3409},
  intrahash = {81474f00e1605d45462e23f743dc88bb},
  issn = {1540-6237},
  journal = {Social Science Quarterly},
  number = 3,
  pages = {690--706},
  publisher = {Blackwell Publishing Inc},
  title = {Measuring Media Bias: A Content Analysis of Time and Newsweek Coverage of Domestic Social Issues, 1975–2000*},
  url = {http://dx.doi.org/10.1111/j.1540-6237.2007.00478.x},
  volume = 88,
  year = 2007
}

@book{atzmueller2013ubiquitous,
  address = {Berlin, Heidelberg},
  editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas},
  interhash = {b0fcec93b875c8b0060087bc07944e89},
  intrahash = {1e2d036351662d35ef95719554d37e46},
  isbn = {9783642453915 3642453910 9783642453922 3642453929},
  publisher = {Imprint: Springer},
  refid = {867052137},
  title = {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},
  url = {http://link.springer.com/book/10.1007/978-3-642-45392-2},
  year = 2013
}

@incollection{mitzlaff2013semantics,
  address = {Heidelberg, Germany},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Complex Networks IV},
  doi = {10.1007/978-3-642-36844-8_2},
  editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert},
  interhash = {bf333426bb7af5f01bf0c465c1bfe1fc},
  intrahash = {0a35f1ed66fcd342a6a44d70c63fb735},
  optisbn = {978-3-642-36843-1},
  opturl = {http://dx.doi.org/10.1007/978-3-642-36844-8_2},
  publisher = {Springer Verlag},
  series = {Studies in Computational Intelligence},
  title = {{Semantics of User Interaction in Social Media}},
  volume = 476,
  year = 2013
}

@article{atzmueller2013exploratory,
  author = {Atzmueller, Martin and Lemmerich, Florian},
  interhash = {6e83bf4017fffe31a5632289d91c1b6d},
  intrahash = {9f176520035c05191d77ebd53803b817},
  journal = {International Journal of Web Science (Special Issue on Social Web Search and Mining)},
  number = {1/2},
  title = {{Exploratory Pattern Mining on Social Media using Geo-References and Social Tagging Information}},
  volume = 2,
  year = 2013
}

@article{schulz2013rechtliche,
  author = {Schulz, Thomas and Skistims, Hendrik and Zirfas, Julia and Atzmueller, Martin and Scholz, Christoph},
  interhash = {224ac0c2ca760bea1ccdb5ee66959a8b},
  intrahash = {2ec777edc0adc9f74292caf4ae9ad4f0},
  journal = {ZD},
  pages = {60--65},
  title = {{Rechtliche Ausgestaltung sozialer Konferenzplattformen}},
  volume = 2,
  year = 2013
}

@incollection{atzmueller2013social,
  address = {Heidelberg, Germany},
  author = {Atzmueller, Martin},
  booktitle = {Mobile Social Networking: An Innovative Approach},
  editor = {Chin, Alvin and Zhang, Daqing},
  interhash = {1be75b604acbaf39653eeca9833782df},
  intrahash = {cd910f3a16c9368e7b73407708452653},
  publisher = {Springer Verlag},
  title = {{Social Behavior in Mobile Social Networks: Characterizing Links, Roles and Communities}},
  year = 2013
}

@incollection{MASH:13,
  address = {Heidelberg, Germany},
  author = {Mitzlaff, Folke and Atzmueller, Martin and Stumme, Gerd and Hotho, Andreas},
  booktitle = {Complex Networks IV},
  doi = {10.1007/978-3-642-36844-8_2},
  editor = {Ghoshal, Gourab and Poncela-Casasnovas, Julia and Tolksdorf, Robert},
  interhash = {bf333426bb7af5f01bf0c465c1bfe1fc},
  intrahash = {0a35f1ed66fcd342a6a44d70c63fb735},
  optisbn = {978-3-642-36843-1},
  opturl = {http://dx.doi.org/10.1007/978-3-642-36844-8_2},
  publisher = {Springer Verlag},
  series = {Studies in Computational Intelligence},
  title = {{Semantics of User Interaction in Social Media}},
  volume = 476,
  year = 2013
}

@article{Atzmueller:12,
  author = {Atzmueller, Martin},
  interhash = {ce21e72d189207dbee58420af81efca8},
  intrahash = {a66dc503e2f90d0a484e0dbef5febcd3},
  journal = {Informatik Spektrum},
  number = 2,
  pages = {132-135},
  title = {Mining Social Media},
  volume = 35,
  year = 2012
}

@incollection{LA:12,
  address = {Heidelberg, Germany},
  alteditor = {Editor},
  author = {Lemmerich, Florian and Atzmueller, Martin},
  booktitle = {{Modeling and Mining Ubiquitous Social Media}},
  interhash = {ae9dc5bfe7f42f1e9ca59aadda4bfd9e},
  intrahash = {8435f029bf0e32340e2cf44d0eeb65e7},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {{Describing Locations using Tags and Images: Explorative Pattern Mining in Social Media}},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/lemmerich-explorative-pattern-mining-socia-media-lnai-2012.pdf},
  volume = 7472,
  year = 2012
}

@proceedings{CAH:12,
  address = {New York, NY, USA},
  editor = {Chin, Alvin and Atzmueller, Martin and Helic, Denis},
  interhash = {8c21f6c9eb9d4658ca2374f12a63e950},
  intrahash = {338ca2515a0f6cd77996d1bbf54c7f4d},
  publisher = {ACM Press},
  title = {{Proceedings MSM 2012: Workshop on Modeling Social Media -- Collective Intelligence in Social Media}},
  year = 2012
}

@book{ACHH:12,
  address = {Heidelberg, Germany},
  editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas},
  interhash = {ebf8e8b66c6c0723092e11e40998d61f},
  intrahash = {a0e5d144b39199fa4acb6319f29e7a15},
  publisher = {Springer Verlag},
  series = {Lecture Notes in Computer Science},
  title = {Modeling and Mining Ubiquitous Social Media},
  url = {http://www.springer.com/computer/ai/book/978-3-642-33683-6},
  volume = 7472,
  year = 2012
}

@incollection{ADHMS:12,
  address = {Heidelberg, Germany},
  alteditor = {Editor},
  author = {Atzmueller, Martin and Doerfel, Stephan and Hotho, Andreas and Mitzlaff, Folke and Stumme, Gerd},
  booktitle = {{Modeling and Mining Ubiquitous Social Media}},
  interhash = {4f1f4b515b01cc448a91b3e368deabad},
  intrahash = {d81d6f6ccdf3ff6572898d39c90e6354},
  publisher = {Springer Verlag},
  series = {LNAI},
  title = {Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles},
  url = {http://www.kde.cs.uni-kassel.de/atzmueller/paper/atzmueller-face-to-face-contacts-dynamics-lnai-2012.pdf},
  volume = 7472,
  year = 2012
}

@book{ACHH:12,
  address = {Heidelberg, Germany},
  editor = {Atzmueller, Martin and Chin, Alvin and Helic, Denis and Hotho, Andreas},
  interhash = {ebf8e8b66c6c0723092e11e40998d61f},
  intrahash = {a0e5d144b39199fa4acb6319f29e7a15},
  publisher = {Springer Verlag},
  series = {Lecture Notes in Computer Science},
  title = {Modeling and Mining Ubiquitous Social Media},
  url = {http://www.springer.com/computer/ai/book/978-3-642-33683-6},
  volume = 7472,
  year = 2012
}

@inproceedings{agichtein2008finding,
  abstract = {The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humans},
  acmid = {1341557},
  address = {New York, NY, USA},
  author = {Agichtein, Eugene and Castillo, Carlos and Donato, Debora and Gionis, Aristides and Mishne, Gilad},
  booktitle = {Proceedings of the international conference on Web search and web data mining},
  doi = {10.1145/1341531.1341557},
  interhash = {72c7bf5d1c983c47bfc3c6cc9084c26c},
  intrahash = {29c5c74d95dce215a9692b94fc619839},
  isbn = {978-1-59593-927-2},
  location = {Palo Alto, California, USA},
  numpages = {12},
  pages = {183--194},
  publisher = {ACM},
  title = {Finding high-quality content in social media},
  url = {http://doi.acm.org/10.1145/1341531.1341557},
  year = 2008
}

@inproceedings{brew2010using,
  abstract = {Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsource Crowdsourcing is a term, sometimes associated with Web 2.0 technologies, that describes outsourcing of tasks to a large often anonymous community. much of the annotation work that is required to train a machine learning system to perform sentiment scoring. We describe such a system for tracking economic sentiment in online media that has been deployed since August 2009. It uses annotations provided by a cohort of non-expert annotators to train a learning system to classify a large body of news items. We report on the design challenges addressed in managing the effort of the annotators and in making annotation an interesting experience.},
  acmid = {1860997},
  address = {Amsterdam, The Netherlands, The Netherlands},
  author = {Brew, Anthony and Greene, Derek and Cunningham, Pádraig},
  booktitle = {Proceedings of the 19th European Conference on Artificial Intelligence},
  editor = {Coelho, Helder and Studer, Rudi and Wooldridge, Michael},
  interhash = {90650749ea1084b729710d37b5865b72},
  intrahash = {9643e3c5729886b0b4e85cb3d3d704f5},
  isbn = {978-1-60750-605-8},
  numpages = {6},
  pages = {145--150},
  publisher = {IOS Press},
  series = {Frontiers in Artificial Intelligence and Applications},
  title = {Using Crowdsourcing and Active Learning to Track Sentiment in Online Media},
  url = {http://dl.acm.org/citation.cfm?id=1860967.1860997},
  volume = 215,
  year = 2010
}

@proceedings{conf/ht/2010msmmuse,
  booktitle = {MSM/MUSE},
  editor = {Atzmueller, Martin and Hotho, Andreas and Strohmaier, Markus and Chin, Alvin},
  ee = {http://dx.doi.org/10.1007/978-3-642-23599-3},
  interhash = {2be9c4f31fd94e24d902520195b653d3},
  intrahash = {4cf42ebabd9a670c70bee456affda285},
  isbn = {978-3-642-23598-6},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Analysis of Social Media and Ubiquitous Data - International Workshops MSM 2010, Toronto, Canada, June 13, 2010, and MUSE 2010, Barcelona, Spain, September 20, 2010, Revised Selected Papers},
  url = {http://dblp.uni-trier.de/db/conf/ht/msmmuse2010.html},
  volume = 6904,
  year = 2011
}

@inproceedings{clements2007personalization,
  abstract = {This article describes a framework that captures collaborative tagging systems, and derives from it an overview of user tasks that qualify for personalization in such a system. Major research areas have focused on some of these tasks, but we identify many more opportunities. We propose a collaborative model that combines collaborative filtering and information retrieval techniques in order to assists the user to achieve these tasks. Based only on the user's tags, this personalization model assumes that a user's tags identify this user's taste. Because many users do not only tag the content that matches their taste, we propose an evaluating experiment that shows if rating information can be used to adjust the users' taste profiles. This experiment is one of the steps to advance to a completely personalized model, integrating user preference, content annotations and people relations.},
  author = {Clements, M.},
  booktitle = {Proceedings of BCS IRSG Symposium: Future Directions in Information Access 2007},
  interhash = {4e817e20bc7caf0a8e1111e882700383},
  intrahash = {fe43da7e093f06c36010358724d03b7b},
  location = {Glasgow, UK},
  month = aug,
  title = {Personalization of Social Media},
  year = 2007
}

@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
}