@article{leydesdorff2012alternatives,
  abstract = {Journal Impact Factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over two years. However, it has been recognized that citation distributions vary among fields of science and that one needs to normalize for this. Furthermore, the mean-or any central-tendency statistics-is not a good representation of the citation distribution because these distributions are skewed. Important steps have been taken to solve these two problems during the last few years. First, one can normalize at the article level using the citing audience as the reference set. Second, one can use non-parametric statistics for testing the significance of differences among ratings. A proportion of most-highly cited papers (the top-10% or top-quartile) on the basis of fractional counting of the citations may provide an alternative to the current IF. This indicator is intuitively simple, allows for statistical testing, and accords with the state of the art.
},
  added-at = {2012-01-24T12:13:30.000+0100},
  author = {Leydesdorff, Loet},
  biburl = {http://www.bibsonomy.org/bibtex/2bd589cc0b6fdfc74b5eea4262c46d3a4/jaeschke},
  description = {Scientometrics, in press},
  interhash = {8d14f862a94fb45d31172f8d2a6485fa},
  intrahash = {bd589cc0b6fdfc74b5eea4262c46d3a4},
  journal = {Digital Libraries},
  keywords = {analysis citation factor impact toread scientometrics},
  timestamp = {2012-01-24T12:13:30.000+0100},
  title = {Alternatives to the Journal Impact Factor: I3 and the Top-10% (or
  Top-25%?) of the Most-Highly Cited Papers},
  url = {http://arxiv.org/abs/1201.4638},
  volume = {1201.4638},
  year = 2012
}

@inproceedings{gaugaz2012predicting,
  abstract = {The amount of news content on the Web is increasing: Users can access news articles coming from a variety of sources on the Web: from newswires, news agencies, blogs, and at various places, e.g. even within Web search engines result pages. Anyhow, it still is a challenge for current search engines to decide which news events are worth being shown to the user (either for a newsworthy query or in a news portal). In this paper we define the task of predicting the future impact of news events. Being able to predict event impact will, for example, enable a newspaper to decide whether to follow a specific event or not, or a news search engine which stories to display. We define a flexible framework that, given some definition of impact, can predict its future development at the beginning of the event. We evaluate several possible definitions of event impact and experimentally identify the best features for each of them.},
  added-at = {2011-11-29T11:17:53.000+0100},
  author = {Gaugaz, Julien and Siehndel, Patrick and Demartini, Gianluca and Iofciu, Tereza and Georgescu, Mihai and Henze, Nicola},
  biburl = {http://www.bibsonomy.org/bibtex/2f29c05f9a4fc3bb2189a965d95f622f9/jaeschke},
  booktitle = {Proc. of the 34th European Conference on Information Retrieval (ECIR 2012)},
  interhash = {dc898856b5a18bf1cb9307d1bd9b5268},
  intrahash = {f29c05f9a4fc3bb2189a965d95f622f9},
  keywords = {event impact news prediction toread},
  location = {Barcelona, Spain},
  month = apr,
  timestamp = {2011-11-29T11:17:53.000+0100},
  title = {Predicting the Future Impact of News Events},
  url = {http://www.l3s.de/web/page25g.do?kcond12g.att1=1833},
  year = 2012
}

@inproceedings{bollen2009suggestions,
  abstract = {Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource.  The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. We present experimental results that show that the power law distribution forms regardless of whether or not tag suggestions are presented to the users.},
  added-at = {2009-06-29T11:13:35.000+0200},
  address = {New York, NY, USA},
  author = {Bollen, Dirk and Halpin, Harry},
  biburl = {http://www.bibsonomy.org/bibtex/2d7b14a0eb7fabb3cee8846802de069fe/jaeschke},
  booktitle = {HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia},
  interhash = {280a97ee745f4e0409cf031a1b7ea247},
  intrahash = {d7b14a0eb7fabb3cee8846802de069fe},
  keywords = {folksonomy ht09 impact recommender tag},
  month = {July},
  paperid = {pp161},
  publisher = {ACM},
  session = {Poster},
  timestamp = {2009-06-29T11:13:35.000+0200},
  title = {The Role of Tag Suggestions in Folksonomies},
  year = 2009
}