@inproceedings{kwak2010twitter,
  address = {New York, NY, USA},
  author = {Kwak, Haewoon and Lee, Changhyun and Park, Hosung and Moon, Sue},
  booktitle = {WWW '10: Proceedings of the 19th international conference on World wide web},
  doi = {http://doi.acm.org/10.1145/1772690.1772751},
  interhash = {a0a85d868b49b468ff7b8011b1353b73},
  intrahash = {a94c8b55acd6eb165be451d8cc6ee63b},
  isbn = {978-1-60558-799-8},
  location = {Raleigh, North Carolina, USA},
  pages = {591--600},
  publisher = {ACM},
  title = {{W}hat is {T}witter, a social network or a news media?},
  year = 2010
}

@inproceedings{GKRS09,
  author = {Günther, Oliver and Krasnova, Hanna and Riehle, Dirk and Schöndienst, Valentin},
  booktitle = {In Proceedings of the Fifteenth Americas Conference on Information Systems (AMCIS 2009), forthcoming},
  interhash = {4ad9579fe06dbfb5f73e8e2203f95377},
  intrahash = {7cf1ca814c67427ad22b82ac4784c6dd},
  title = {Modeling Micro-Blogging Adoption in the Enterprise},
  url = {http://dirkriehle.com/2009/04/20/modeling-micro-blogging-adoption-in-the-enterprise/},
  year = 2009
}

@inproceedings{naaman2010really,
  abstract = {In this work we examine the characteristics of social activity and patterns of communication on Twitter, a prominent example of the emerging class of communication systems we call "social awareness streams." We use system data and message content from over 350 Twitter users, applying human coding and quantitative analysis to provide a deeper understanding of the activity of individuals on the Twitter network. In particular, we develop a content-based categorization of the type of messages posted by Twitter users, based on which we examine users' activity. Our analysis shows two common types of user behavior in terms of the content of the posted messages, and exposes differences between users in respect to these activities.},
  acmid = {1718953},
  address = {New York, NY, USA},
  author = {Naaman, Mor and Boase, Jeffrey and Lai, Chih-Hui},
  booktitle = {Proceedings of the 2010 ACM conference on Computer supported cooperative work},
  doi = {10.1145/1718918.1718953},
  interhash = {c6bf200b6532c60e125ba45d17c8d4cf},
  intrahash = {1066e1c098a8d7b67e7903069c6a7a9f},
  isbn = {978-1-60558-795-0},
  location = {Savannah, Georgia, USA},
  numpages = {4},
  pages = {189--192},
  publisher = {ACM},
  series = {CSCW '10},
  title = {Is it really about me?: message content in social awareness streams},
  url = {http://doi.acm.org/10.1145/1718918.1718953},
  year = 2010
}

@misc{huberman2008social,
  abstract = {  Scholars, advertisers and political activists see massive online socialnetworks as a representation of social interactions that can be used to studythe propagation of ideas, social bond dynamics and viral marketing, amongothers. But the linked structures of social networks do not reveal actualinteractions among people. Scarcity of attention and the daily rythms of lifeand work makes people default to interacting with those few that matter andthat reciprocate their attention. A study of social interactions within Twitterreveals that the driver of usage is a sparse and hidden network of connectionsunderlying the declared set of friends and followers.},
  author = {Huberman, Bernardo A. and Romero, Daniel M. and Wu, Fang},
  interhash = {15d255f636e73eb2c235393752bffb88},
  intrahash = {56f3e4aa66e76b8a914c5d89a2364f96},
  note = {cite arxiv:0812.1045},
  title = {Social networks that matter: Twitter under the microscope},
  url = {http://arxiv.org/abs/0812.1045},
  year = 2008
}

@inproceedings{wagner2010wisdom,
  abstract = {Although one might argue that little wisdom can be conveyed in messages of 140 characters or less, this paper sets out to explore whether the aggregation of messages in social awareness streams, such as Twitter, conveys meaningful information about a given domain. As a research community, we know little about the structural and semantic properties of such streams, and how they can be analyzed, characterized and used. This paper introduces a network-theoretic model of social awareness stream, a so-called \tweetonomy", together with a set of stream-based measures that allow researchers to systematically define and compare different stream aggregations. We apply the model and measures to a dataset acquired from Twitter to study emerging semantics in selected streams. The network-theoretic model and the corresponding measures introduced in this paper are relevant for researchers interested in information retrieval and ontology learning from social awareness streams. Our empirical findings demonstrate that different social awareness stream aggregations exhibit interesting differences, making them amenable for different applications.},
  author = {Wagner, C. and Strohmaier, M.},
  booktitle = {Proc. of the Semantic Search 2010 Workshop (SemSearch2010)},
  file = {wagner2010wisdom.pdf:wagner2010wisdom.pdf:PDF},
  groups = {public},
  interhash = {02c222a4f9abd5964ea61af034769af4},
  intrahash = {2f96232a648d4fd1617c389d899f3d2b},
  location = {Raleigh, NC, USA},
  month = {april},
  timestamp = {2010-04-19 08:03:47},
  title = {The Wisdom in Tweetonomies: Acquiring Latent Conceptual Structures from Social Awareness Streams},
  url = {http://mstrohm.wordpress.com/2010/04/17/on-taxonomies-folksonomies-and-tweetonomies/},
  username = {dbenz},
  year = 2010
}