@article{brody,
	Author = {Brody, Tim and Harnad, Stevan and Carr, Leslie},
	Date-Modified = {2012-01-23 19:07:47 -0500},
	Journal = {Journal of the American Society for Information Science and Technology},
	Number = {8},
	Pages = {1060--1072},
	Title = {Earlier Web usage statistics as predictors of later citation impact},
	Volume = {57},
	Year = {2006},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/asi.20373}}

@article{Eysenbach2011,
	Author = {Eysenbach, Gunther},
	Journal = {Journal of Medical Internet Research},
	Number = {4},
	Pages = {123},
	Title = {Can Tweets Predict Citations? Metrics of Social Impact Based on Twitter and Correlation with Traditional Metrics of Scientific Impact},
	Volume = {13},
	Year = {2011}}

@inproceedings{weller:2011,
	Author = {Katrin Weller and Evelyn Dr\"oge and Cornelius Puschmann},
	Booktitle = {Making Sense of Microposts {(\#MSM2011)}},
	Date-Added = {2012-01-23 18:43:25 -0500},
	Date-Modified = {2012-01-23 18:54:45 -0500},
	Pages = {1--12},
	Title = {Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences},
	Year = 2011,
	Bdsk-Url-1 = {http://ceur-ws.org/Vol-718/paper_04.pdf}}

@article{golder,
	Abstract = {We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses---which is consistent with the effects of sleep and circadian rhythm---and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.},
	Author = {Golder, Scott A. and Macy, Michael W.},
	Date-Added = {2012-01-23 18:12:00 -0500},
	Date-Modified = {2012-01-23 19:07:47 -0500},
	Eprint = {http://www.sciencemag.org/content/333/6051/1878.full.pdf},
	Journal = {Science},
	Number = {6051},
	Pages = {1878-1881},
	Title = {Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures},
	Volume = {333},
	Year = {2011},
	Bdsk-Url-1 = {http://www.sciencemag.org/content/333/6051/1878.abstract},
	Bdsk-Url-2 = {http://dx.doi.org/10.1126/science.1202775}}

@inproceedings{modeling,
	Address = {Barcelona, Spain.},
	Author = {Johan Bollen and Huina Mao and Alberto Pepe},
	Booktitle = {Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM 2011)},
	Date-Added = {2012-01-23 18:08:07 -0500},
	Date-Modified = {2012-01-23 18:08:07 -0500},
	Note = {\url{http://arxiv.org/abs/0911.1583}},
	Title = {Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena.},
	Year = {2011}}

@article{crandall,
	Abstract = {We investigate the extent to which social ties between people can be inferred from co-occurrence in time and space: Given that two people have been in approximately the same geographic locale at approximately the same time, on multiple occasions, how likely are they to know each other? Furthermore, how does this likelihood depend on the spatial and temporal proximity of the co-occurrences? Such issues arise in data originating in both online and offline domains as well as settings that capture interfaces between online and offline behavior. Here we develop a framework for quantifying the answers to such questions, and we apply this framework to publicly available data from a social media site, finding that even a very small number of co-occurrences can result in a high empirical likelihood of a social tie. We then present probabilistic models showing how such large probabilities can arise from a natural model of proximity and co-occurrence in the presence of social ties. In addition to providing a method for establishing some of the first quantifiable estimates of these measures, our findings have potential privacy implications, particularly for the ways in which social structures can be inferred from public online records that capture individuals' physical locations over time.},
	Author = {Crandall, David J. and Backstrom, Lars and Cosley, Dan and Suri, Siddharth and Huttenlocher, Daniel and Kleinberg, Jon},
	Date-Added = {2012-01-23 18:06:53 -0500},
	Date-Modified = {2012-01-23 19:07:47 -0500},
	Eprint = {http://www.pnas.org/content/107/52/22436.full.pdf+html},
	Journal = {Proceedings of the National Academy of Sciences},
	Number = {52},
	Pages = {22436-22441},
	Title = {Inferring social ties from geographic coincidences},
	Volume = {107},
	Year = {2010},
	Bdsk-Url-1 = {http://www.pnas.org/content/107/52/22436.abstract},
	Bdsk-Url-2 = {http://dx.doi.org/10.1073/pnas.1006155107}}

@article{dunbar,
	Abstract = {
        <p>Microblogging and mobile devices appear to augment human social capabilities, which raises the question whether they remove cognitive or biological constraints on human communication. In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar's number. We find that the data are in agreement with Dunbar's result; users can entertain a maximum of 100--200 stable relationships. Thus, the `economy of attention' is limited in the online world by cognitive and biological constraints as predicted by Dunbar's theory. We propose a simple model for users' behavior that includes finite priority queuing and time resources that reproduces the observed social behavior.</p>
      },
	Author = {Gon{\c c}alves, Bruno AND Perra, Nicola AND Vespignani, Alessandro},
	Date-Added = {2012-01-23 18:06:18 -0500},
	Date-Modified = {2012-01-23 19:07:47 -0500},
	Journal = {PLoS ONE},
	Month = {08},
	Number = {8},
	Pages = {e22656},
	Publisher = {Public Library of Science},
	Title = {Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number},
	Volume = {6},
	Year = {2011},
	Bdsk-Url-1 = {http://dx.doi.org/10.1371/journal.pone.0022656}}

@book{rubin,
	Address = {New York},
	Author = {Rubin, Richard},
	Edition = {3rd},
	Isbn = {9781555706906},
	Publisher = {Neal-Schuman Publishers},
	Title = {Foundations of library and information science},
	Year = {2010}}

@article{thelwall,
	Author = {Mike Thelwall},
	Journal = {Journal of Information Science},
	Number = {4},
	Pages = {605 --621},
	Title = {Bibliometrics to webometrics},
	Volume = {34},
	Year = {2008}}

@article{clickstream,
	Author = {Johan Bollen and Herbert {Van de Sompel} and Aric Hagberg and Luis Bettencourt and Ryan Chute and Marko A. Rodriguez and Lyudmila Balakireva},
	Journal = {Plos One},
	Number = {3},
	Title = {Clickstream data yields high-resolution maps of science},
	Volume = {4},
	Year = {2009}}

@inbook{usagebibliometrics,
	Address = {Medford, NJ},
	Author = {Michael Kurtz and Bollen, J.},
	Booktitle = {Annual Review of Information Science and Technology},
	Date-Added = {2012-01-16 16:44:11 -0500},
	Date-Modified = {2012-01-16 16:44:32 -0500},
	Isbn = {978-1-57387-371-0},
	Keywords = {undefined},
	Month = {01/2010},
	Organization = {Information Today, Inc.},
	Pages = {3-64},
	Publisher = {Information Today, Inc.},
	Title = {Usage Bibliometrics},
	Volume = {44},
	Year = {2010}}

@article{pepe,
	Author = {Alberto Pepe and Corinna DiGennaro},
	Date-Added = {2009-11-16 13:09:46 -0800},
	Date-Modified = {2010-07-12 13:29:33 +0200},
	Journal = {First Monday},
	Note = {\url{http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/viewArticle/2740/2406}},
	Number = {12},
	Read = {Yes},
	Title = {{Political protest Italian-Style: The blogosphere and mainstream media in the promotion and coverage of Beppe Grillo's V-day}},
	Volume = {14},
	Year = {2009}}

@conference{jcdl,
	Address = {Pittsburgh, PA},
	Author = {Johan Bollen and Herbert {Van de Sompel} and Marko A. Rodriguez},
	Booktitle = {JCDL 2008},
	Month = {June},
	Title = {Towards usage-based impact metrics: first results from the {MESUR} project},
	Year = {2008}}

@article{jason,
	Author = {Jason Priem and Bradely H. Hemminger},
	Journal = {First Monday},
	Number = {7},
	Title = {Scientometrics 2.0: New metrics of scholarly impact on the social Web},
	Volume = {15},
	Year = {2010}}

@conference{katrin,
	Address = {Greece},
	Booktitle = {1st Workshop on Making Sense of Microposts},
	Month = {May},
	Title = {Citation Analysis in Twitter: Approaches for Defining and Measuring Information Flows within Tweets during Scientific Conferences},
	Year = {2011}}

@conference{martin,
	Author = {Martin Ebner and Wolfgang Reinhardt},
	Booktitle = {Proceedings of the 1st International Workshop on Science (2009)},
	Pages = {1--8},
	Volume = {2},
	Year = {2009}}

@conference{julie,
	Address = {Raleigh, NC},
	Author = {Julie Letierce and Alexandre Passant and Stefan Decker and John G. Breslin},
	Booktitle = {Web Science Conf. 2010},
	Month = {April},
	Title = {Understanding how Twitter is used to spread scientific messages},
	Year = {2010}}

@conference{priem,
	Author = {Jason Priem and Kaitlin Light Costello},
	Booktitle = {Proceedings of the American Society for Information Science and Technology},
	Issue = {1},
	Pages = {1--4},
	Title = {How and why scholars cite on Twitter},
	Volume = {47},
	Year = {2010}}

@conference{weller,
	Address = {Germany},
	Author = {Katrin Weller and Cornelius Puschmann},
	Booktitle = {Web Science Conf. 2011},
	Month = {June},
	Title = {Twitter for Scientific Communication: How Can Citations/References be Identified and Measured?},
	Year = {2011}}

@conference{altmetrics,
	Address = {New Orleans, LA},
	Author = {Jason Priem and Heather A. Piwowar and Bradley H. Hemminger},
	Booktitle = {Metrics 2011: Symposium on Informetric and Scientometric Research},
	Month = {October},
	Title = {Altmetrics in the wild: An exploratory study of impact metrics based on social media},
	Year = {2011}}
