@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
}

@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
}

@inproceedings{Keally:2011:PTP:2070942.2070968,
  abstract = {The vast array of small wireless sensors is a boon to body sensor network applications, especially in the context awareness and activity recognition arena. However, most activity recognition deployments and applications are challenged to provide personal control and practical functionality for everyday use. We argue that activity recognition for mobile devices must meet several goals in order to provide a practical solution: user friendly hardware and software, accurate and efficient classification, and reduced reliance on ground truth. To meet these challenges, we present PBN: Practical Body Networking. Through the unification of TinyOS motes and Android smartphones, we combine the sensing power of on-body wireless sensors with the additional sensing power, computational resources, and user-friendly interface of an Android smartphone. We provide an accurate and efficient classification approach through the use of ensemble learning. We explore the properties of different sensors and sensor data to further improve classification efficiency and reduce reliance on user annotated ground truth. We evaluate our PBN system with multiple subjects over a two week period and demonstrate that the system is easy to use, accurate, and appropriate for mobile devices.},
  acmid = {2070968},
  address = {New York, NY, USA},
  author = {Keally, Matthew and Zhou, Gang and Xing, Guoliang and Wu, Jianxin and Pyles, Andrew},
  booktitle = {Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems},
  doi = {10.1145/2070942.2070968},
  interhash = {5e6a13d34026f65338cfa619054822c8},
  intrahash = {61e5e4559d031c4152b3f316c0aa5209},
  isbn = {978-1-4503-0718-5},
  location = {Seattle, Washington},
  numpages = {14},
  pages = {246--259},
  publisher = {ACM},
  series = {SenSys '11},
  title = {PBN: towards practical activity recognition using smartphone-based body sensor networks},
  url = {http://doi.acm.org/10.1145/2070942.2070968},
  year = 2011
}

@misc{Kim2012,
  abstract = {  Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains. },
  author = {Kim, Seungyeon and Li, Fuxin and Lebanon, Guy and Essa, Irfan},
  interhash = {78c5eda9e1ef2780d70234dc4942203f},
  intrahash = {d169c08d5241a0912f3d60c97d87e2c0},
  note = {cite arxiv:1202.1568Comment: 15 pages, 7 figures},
  title = {Beyond Sentiment: The Manifold of Human Emotions},
  url = {http://arxiv.org/abs/1202.1568},
  year = 2012
}

@inproceedings{Bethard:2010:ICL:1871437.1871517,
  acmid = {1871517},
  address = {New York, NY, USA},
  author = {Bethard, Steven and Jurafsky, Dan},
  booktitle = {Proceedings of the 19th ACM international conference on Information and knowledge management},
  doi = {http://doi.acm.org/10.1145/1871437.1871517},
  interhash = {1cdf6c7da38af251279e9fb915266af2},
  intrahash = {369206c7472baeaa5ecefef586e16c6a},
  isbn = {978-1-4503-0099-5},
  location = {Toronto, ON, Canada},
  numpages = {10},
  pages = {609--618},
  publisher = {ACM},
  series = {CIKM '10},
  title = {Who should I cite: learning literature search models from citation behavior},
  url = {http://doi.acm.org/10.1145/1871437.1871517},
  year = 2010
}

@inproceedings{anagnostopoulos2011authority,
  author = {Anagnostopoulos, Aris and Brova, George and Terzi, Evimaria},
  booktitle = {Proceedings of the ECML/PKDD 2011},
  interhash = {4b69d0de5d0c542404c9eb387abb0ac2},
  intrahash = {eb4553d07c2975a62fff33e92646a7df},
  title = {Peer and Authority Pressure in Information-Propagation Models},
  year = 2011
}

@article{journals/www/EdaYUU09,
  author = {Eda, Takeharu and Yoshikawa, Masatoshi and Uchiyama, Toshio and Uchiyama, Tadasu},
  ee = {http://dx.doi.org/10.1007/s11280-009-0069-1},
  interhash = {a560796c977bc7582017f662bf88c16d},
  intrahash = {ec3c256e7d1f24cd9d407d3ce7e41d96},
  journal = {World Wide Web},
  number = 4,
  pages = {421-440},
  title = {The Effectiveness of Latent Semantic Analysis for Building Up a Bottom-up Taxonomy from Folksonomy Tags.},
  url = {http://dblp.uni-trier.de/db/journals/www/www12.html#EdaYUU09},
  volume = 12,
  year = 2009
}

@article{JamesSinclair02012008,
  abstract = {The weighted list, known popularly as a `tag cloud', has appeared on many popular folksonomy-based web-sites. Flickr, Delicious, Technorati and many   others have all featured a tag cloud at some point in their history. However, it is unclear whether the tag cloud is actually useful as an aid to finding  information. We conducted an experiment, giving participants the option of using  a tag cloud or a traditional search interface to answer various questions. We found that where the information-seeking task required specific information, participants preferred the search interface. Conversely, where the   information-seeking task was more general, participants preferred the tag cloud. While the tag cloud is not without value, it is not sufficient as the sole means  of navigation for a folksonomy-based dataset.
},
  author = {Sinclair, James and Cardew-Hall, Michael},
  doi = {10.1177/0165551506078083},
  eprint = {http://jis.sagepub.com/cgi/reprint/34/1/15.pdf},
  interhash = {9781d30a620fe81d1b6b6b06925393ab},
  intrahash = {1cc0b296c0af7c80feea7b3bb1bf825c},
  journal = {Journal of Information Science},
  number = 1,
  pages = {15-29},
  title = {{The folksonomy tag cloud: when is it useful?}},
  url = {http://jis.sagepub.com/cgi/content/abstract/34/1/15},
  volume = 34,
  year = 2008
}

@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
}

@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{carpena:035102,
  author = {Carpena, P. and Bernaola-Galv\'{a}n, P. and Hackenberg, M. and Coronado, A. V. and Oliver, J. L.},
  doi = {10.1103/PhysRevE.79.035102},
  eid = {035102},
  interhash = {3444159872c65ea89d007d1838686acc},
  intrahash = {34dcb1eee3ffa31ff4eb77087343c146},
  journal = {Physical Review E (Statistical, Nonlinear, and Soft Matter Physics)},
  number = 3,
  numpages = {4},
  pages = 035102,
  publisher = {APS},
  title = {Level statistics of words: Finding keywords in literary texts and symbolic sequences},
  url = {http://bioinfo2.ugr.es/TextKeywords/},
  volume = 79,
  year = 2009
}

@article{keyhere,
  abstract = {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.},
  author = {Baeza-Yates, Ricardo and Calderón-Benavides, Liliana and González-Caro, Cristina},
  interhash = {92e5f2f5208b5ce2f066dd361ae15758},
  intrahash = {27c7357d3337d890fef53168dce9ed33},
  journal = {String Processing and Information Retrieval},
  pages = {98--109},
  title = {The Intention Behind Web Queries},
  url = {http://dx.doi.org/10.1007/11880561_9},
  year = 2006
}

@article{Pang2008,
  author = {Pang, Bo and Lee, Lillian},
  date = {July 2008},
  interhash = {7bfd8b20ea5f9fb76e96d71c3155c50c},
  intrahash = {236d4f703fda3dd9457863f28eda56cb},
  isbn = {978-1-60198-150-9},
  journal = {Foundations and Trends® in Information Retrieval},
  number = {1-2},
  pages = {1-135},
  tech = {Now publishers},
  title = {Opinion mining and sentiment analysis},
  url = {http://www.cs.cornell.edu/home/llee/omsa/omsa-published.pdf},
  volume = 2,
  year = 2008
}