@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{benz2011measuring,
  author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus},
  booktitle = {Working Notes of the LWA 2011 - Learning, Knowledge, Adaptation},
  interhash = {33a2078f3836293d71c449d5376fc440},
  intrahash = {923d369285422c758398cbe92e3532cd},
  title = {One Tag to Bind Them All: Measuring Term Abstractness in Social Metadata},
  year = 2011
}

@inproceedings{benz2011measuring,
  abstract = {Recent research has demonstrated how the widespread adoption of collaborative tagging systems yields emergent semantics. In recent years, much has been learned about how to harvest the data produced by taggers for engineering light-weight ontologies. For example, existing measures of tag similarity and tag relatedness have proven crucial step stones for making latent semantic relations in tagging systems explicit. However, little progress has been made on other issues, such as understanding the different levels of tag generality (or tag abstractness), which is essential for, among others, identifying hierarchical relationships between concepts. In this paper we aim to address this gap. Starting from a review of linguistic definitions of word abstractness, we first use several large-scale ontologies and taxonomies as grounded measures of word generality, including Yago, Wordnet, DMOZ and Wikitaxonomy. Then, we introduce and apply several folksonomy-based methods to measure the level of generality of given tags. We evaluate these methods by comparing them with the grounded measures. Our results suggest that the generality of tags in social tagging systems can be approximated with simple measures. Our work has implications for a number of problems related to social tagging systems, including search, tag recommendation, and the acquisition of light-weight ontologies from tagging data.},
  address = {Heraklion, Crete},
  author = {Benz, Dominik and Körner, Christian and Hotho, Andreas and Stumme, Gerd and Strohmaier, Markus},
  booktitle = {Proceedings of the 8th Extended Semantic Web Conference (ESWC 2011)},
  editor = {Antoniou, Grigoris and Grobelnik, Marko and Simperl, Elena and Parsia, Bijan and Plexousakis, Dimitris and Pan, Jeff and Leenheer, Pieter De},
  interhash = {33a2078f3836293d71c449d5376fc440},
  intrahash = {67b4cd173ae1f6d98d80561b5f0289a4},
  month = may,
  title = {One Tag to Bind Them All : Measuring Term Abstractness in Social Metadata},
  url = {http://www.kde.cs.uni-kassel.de/pub/pdf/benz2011measuring.pdf},
  year = 2011
}