@inproceedings{Laniado2010,
  author = {Laniado, David and Mika, Peter},
  booktitle = {International Semantic Web Conference (1)},
  crossref = {conf/semweb/2010-1},
  editor = {Patel-Schneider, Peter F. and Pan, Yue and Hitzler, Pascal and Mika, Peter and Zhang, Lei and Pan, Jeff Z. and Horrocks, Ian and Glimm, Birte},
  ee = {http://dx.doi.org/10.1007/978-3-642-17746-0_30},
  interhash = {3a63f88e11f958d548fa91fe442e1dcf},
  intrahash = {58dace4881efbd12c81ef1cc2e6bf7b9},
  isbn = {978-3-642-17745-3},
  pages = {470-485},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  title = {Making Sense of Twitter.},
  url = {http://dblp.uni-trier.de/db/conf/semweb/iswc2010-1.html#LaniadoM10},
  volume = 6496,
  year = 2010
}

@article{10.1371/journal.pone.0019467,
  abstract = {        <p>Twitter is a free social networking and micro-blogging service that enables its                     millions of users to send and read each other's “tweets,” or                     short, 140-character messages. The service has more than 190 million registered                     users and processes about 55 million tweets per day. Useful information about                     news and geopolitical events lies embedded in the Twitter stream, which                     embodies, in the aggregate, Twitter users' perspectives and reactions to                     current events. By virtue of sheer volume, content embedded in the Twitter                     stream may be useful for tracking or even forecasting behavior if it can be                     extracted in an efficient manner. In this study, we examine the use of                     information embedded in the Twitter stream to (1) track rapidly-evolving public                     sentiment with respect to H1N1 or swine flu, and (2) track and measure actual                     disease activity. We also show that Twitter can be used as a measure of public                     interest or concern about health-related events. Our results show that estimates                     of influenza-like illness derived from Twitter chatter accurately track reported                     disease levels.</p>       },
  author = {Signorini, Alessio and Segre, Alberto Maria and Polgreen, Philip M.},
  doi = {10.1371/journal.pone.0019467},
  interhash = {56b199b8f3a3d085aef43e25b2aca06b},
  intrahash = {6c3bc3dabf1b1d0095774e87b14d3ad2},
  journal = {PLoS ONE},
  month = {05},
  number = 5,
  pages = {e19467},
  publisher = {Public Library of Science},
  title = {The Use of Twitter to Track Levels of Disease Activity and Public                     Concern in the U.S. during the Influenza A H1N1 Pandemic},
  url = {http://dx.doi.org/10.1371%2Fjournal.pone.0019467},
  volume = 6,
  year = 2011
}

@proceedings{proc_msm2011@eswc2011,
  booktitle = {Making Sense of Microposts {(\#MSM2011)}},
  editor = {Rowe, Matthew and Stankovic, Milan and Dadzie, Aba-Sah and Hardey, Mariann},
  interhash = {1590ada2c2ea27847ffd872ba4d8d07d},
  intrahash = {620237891cabce63e8387ece58243f98},
  month = may,
  title = {Proceedings, 1st Workshop on Making Sense of Microposts {(\#MSM2011)}: Big things come in small packages, Heraklion, Crete, Greece, 30th May 2011},
  url = {http://ceur-ws.org/Vol-718},
  year = 2011
}