@article{yan2011spread,
  abstract = {        <p>The presence of web-based communities is a distinctive signature of Web 2.0. The web-based feature means that information propagation within each community is highly facilitated, promoting complex collective dynamics in view of information exchange. In this work, we focus on a community of scientists and study, in particular, how the awareness of a scientific paper is spread. Our work is based on the web usage statistics obtained from the PLoS Article Level Metrics dataset compiled by PLoS. The cumulative number of HTML views was found to follow a long tail distribution which is reasonably well-fitted by a lognormal one. We modeled the diffusion of information by a random multiplicative process, and thus extracted the rates of information spread at different stages after the publication of a paper. We found that the spread of information displays two distinct decay regimes: a rapid downfall in the first month after publication, and a gradual power law decay afterwards. We identified these two regimes with two distinct driving processes: a short-term behavior driven by the fame of a paper, and a long-term behavior consistent with citation statistics. The patterns of information spread were found to be remarkably similar in data from different journals, but there are intrinsic differences for different types of web usage (HTML views and PDF downloads versus XML). These similarities and differences shed light on the theoretical understanding of different complex systems, as well as a better design of the corresponding web applications that is of high potential marketing impact.</p>
      },
  author = {Yan, Koon-Kiu and Gerstein, Mark},
  doi = {10.1371/journal.pone.0019917},
  interhash = {5ff7675888626380767e22ad7f672279},
  intrahash = {221dd554089fd1b1918b345fffbd74ce},
  journal = {PLoS ONE},
  month = {05},
  number = 5,
  pages = {e19917},
  publisher = {Public Library of Science},
  title = {The Spread of Scientific Information: Insights from the Web Usage Statistics in PLoS Article-Level Metrics},
  url = {http://dx.doi.org/10.1371%2Fjournal.pone.0019917},
  volume = 6,
  year = 2011
}

@article{brody2006earlier,
  abstract = {The use of citation counts to assess the impact of research articles is well established. However, the citation impact of an article can only be measured several years after it has been published. As research articles are increasingly accessed through the Web, the number of times an article is downloaded can be instantly recorded and counted. One would expect the number of times an article is read to be related both to the number of times it is cited and to how old the article is. The authors analyze how short-term Web usage impact predicts medium-term citation impact. The physics e-print archive—arXiv.org—is used to test this.},
  author = {Brody, Tim and Harnad, Stevan and Carr, Leslie},
  doi = {10.1002/asi.20373},
  interhash = {b4ae997250ae110bcc89826cb2a8205c},
  intrahash = {643ec09ec9d1fd641c0416c3d8dde8f6},
  issn = {1532-2890},
  journal = {Journal of the American Society for Information Science and Technology},
  number = 8,
  pages = {1060--1072},
  publisher = {Wiley Subscription Services, Inc., A Wiley Company},
  title = {Earlier Web usage statistics as predictors of later citation impact},
  url = {http://dx.doi.org/10.1002/asi.20373},
  volume = 57,
  year = 2006
}

@article{bjrneborn2004toward,
  abstract = {In this article, we define webometrics within the framework of informetric studies and bibliometrics, as belonging to library and information science, and as associated with cybermetrics as a generic subfield. We develop a consistent and detailed link typology and terminology and make explicit the distinction among different Web node levels when using the proposed conceptual framework. As a consequence, we propose a novel diagram notation to fully appreciate and investigate link structures between Web nodes in webometric analyses. We warn against taking the analogy between citation analyses and link analyses too far.},
  author = {Björneborn, Lennart and Ingwersen, Peter},
  doi = {10.1002/asi.20077},
  interhash = {1c12570779b33a5a4a0ea079bd7b26a9},
  intrahash = {1ca6a8f4b30106cac0967307f412910f},
  issn = {1532-2890},
  journal = {Journal of the American Society for Information Science and Technology},
  number = 14,
  pages = {1216--1227},
  publisher = {Wiley Subscription Services, Inc., A Wiley Company},
  title = {Toward a basic framework for webometrics},
  url = {http://dx.doi.org/10.1002/asi.20077},
  volume = 55,
  year = 2004
}

@article{taguesutcliffe1992introduction,
  abstract = {The scope and significance of the field of informetrics is defined and related to the earlier fields of bibliometrics and scientometrics. The phenomena studied by informetricians are identified. The major contributors to the field in the past are described and current emphases are related to the contributions in this Special Issue. },
  author = {Tague-Sutcliffe, Jean},
  doi = {http://dx.doi.org/10.1016/0306-4573(92)90087-G},
  interhash = {d0f741e4634dc9881dcf19bd241844b4},
  intrahash = {6573cd7245e0df2962b54440b99608da},
  issn = {0306-4573},
  journal = {Information Processing & Management },
  number = 1,
  pages = {1 - 3},
  title = {An introduction to informetrics },
  url = {http://www.sciencedirect.com/science/article/pii/030645739290087G},
  volume = 28,
  year = 1992
}

@misc{shuai2012scientific,
  abstract = {We analyze the online response of the scientific community to the preprint
publication of scholarly articles. We employ a cohort of 4,606 scientific
articles submitted to the preprint database arXiv.org between October 2010 and
April 2011. We study three forms of reactions to these preprints: how they are
downloaded on the arXiv.org site, how they are mentioned on the social media
site Twitter, and how they are cited in the scholarly record. We perform two
analyses. First, we analyze the delay and time span of article downloads and
Twitter mentions following submission, to understand the temporal configuration
of these reactions and whether significant differences exist between them.
Second, we run correlation tests to investigate the relationship between
Twitter mentions and both article downloads and article citations. We find that
Twitter mentions follow rapidly after article submission and that they are
correlated with later article downloads and later article citations, indicating
that social media may be an important factor in determining the scientific
impact of an article.},
  author = {Shuai, Xin and Pepe, Alberto and Bollen, Johan},
  interhash = {8331e7736f3cc8296cafd7e6397dc010},
  intrahash = {6619e035ee8e7e72ccc9aa32f2acea8e},
  note = {cite arxiv:1202.2461},
  title = {How the Scientific Community Reacts to Newly Submitted Preprints:
  Article Downloads, Twitter Mentions, and Citations},
  url = {http://arxiv.org/abs/1202.2461},
  year = 2012
}

@article{borrego2012measuring,
  abstract = {This paper explores the possibility of using data from social bookmarking services to measure the use of information by academic researchers. Social bookmarking data can be used to augment participative methods (e.g. interviews and surveys) and other, non-participative methods (e.g. citation analysis and transaction logs) to measure the use of scholarly information. We use BibSonomy, a free resource-sharing system, as a case study. Results show that published journal articles are by far the most popular type of source bookmarked, followed by conference proceedings and books. Commercial journal publisher platforms are the most popular type of information resource bookmarked, followed by websites, records in databases and digital repositories. Usage of open access information resources is low in comparison with toll access journals. In the case of open access repositories, there is a marked preference for the use of subject-based repositories over institutional repositories. The results are consistent with those observed in related studies based on surveys and citation analysis, confirming the possible use of bookmarking data in studies of information behaviour in academic settings. The main advantages of using social bookmarking data are that is an unobtrusive approach, it captures the reading habits of researchers who are not necessarily authors, and data are readily available. The main limitation is that a significant amount of human resources is required in cleaning and standardizing the data.},
  author = {Borrego, Ángel and Fry, Jenny},
  doi = {10.1177/0165551512438353},
  eprint = {http://jis.sagepub.com/content/38/3/297.full.pdf+html},
  interhash = {71ddfdd5b3d99b1a2986b4ded5e02b3c},
  intrahash = {e5ccbb3378eeb88e7288d8ce59539812},
  journal = {Journal of Information Science},
  number = 3,
  pages = {297--308},
  title = {Measuring researchers' use of scholarly information through social bookmarking data: A case study of BibSonomy},
  url = {http://jis.sagepub.com/content/38/3/297.abstract},
  volume = 38,
  year = 2012
}

@article{thelwall2012journal,
  abstract = {In theory, the web has the potential to provide information about the wider impact of academic research, beyond traditional scholarly impact. This is because the web can reflect non-scholarly uses of research, such as in online government documents, press coverage or public discussions. Nevertheless, there are practical problems with creating metrics for journals based on web data: principally that most such metrics should be easy for journal editors or publishers to manipulate. Nevertheless, two alternatives seem to have both promise and value: citations derived from digitised books and download counts for journals within specific delivery platforms.},
  affiliation = {Statistical Cybermetrics Research Group, School of Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY UK},
  author = {Thelwall, Mike},
  doi = {10.1007/s11192-012-0669-x},
  interhash = {834707cf0663109f7811a14ae746be72},
  intrahash = {6b4cb7c71daddfe80cce4f7d75fe11d6},
  issn = {0138-9130},
  journal = {Scientometrics},
  keyword = {Computer Science},
  pages = {1-13},
  publisher = {Akadémiai Kiadó, co-published with Springer Science+Business Media B.V., Formerly Kluwer Academic Publishers B.V.},
  title = {Journal impact evaluation: a webometric perspective},
  url = {http://dx.doi.org/10.1007/s11192-012-0669-x},
  year = {2012 (in press)}
}

@article{vaughan2002relationship,
  abstract = {The study found a significant correlation between the number of external links and the journal impact factor for LIS journals. Journals with higher journal impact factor scores tend to attract more links to their Web sites. The study also investigated issues pertaining to data collection methods for webometrics research. It showed that the choice of search engine for data collection could affect the conclusion of a study. Data collected at different time periods were found to be fairly stable. The use of multiple rounds of data collection was shown to be beneficial, especially when the result from a single round of data is borderline significant or inconclusive.},
  author = {Vaughan, Liwen and Hysen, Kathy},
  interhash = {f8a985b736eebc3d58fb82fe78b709ef},
  intrahash = {2dcb5d5265a186cf6bc26833eacc1225},
  journal = {Aslib Proceedings},
  number = 6,
  pages = {356 - 361},
  title = {Relationship between links to journal Web sites and impact factors},
  url = {http://www.emeraldinsight.com/journals.htm?articleid=863971&show=abstract},
  volume = 54,
  year = 2002
}

@article{thelwall2005webometrics,
  author = {Thelwall, Mike and Vaughan, Liwen and Björneborn, Lennart},
  doi = {10.1002/aris.1440390110},
  interhash = {237ab6b776609c4f4b6291e53979980a},
  intrahash = {670a20dea46d1f5c5a0c4792627d3006},
  issn = {1550-8382},
  journal = {Annual Review of Information Science and Technology},
  number = 1,
  pages = {81--135},
  publisher = {Wiley Subscription Services, Inc., A Wiley Company},
  title = {Webometrics},
  url = {http://dx.doi.org/10.1002/aris.1440390110},
  volume = 39,
  year = 2005
}

@electronic{priem2010scientometrics,
  abstract = {The growing flood of scholarly literature is exposing the weaknesses of current, citation-based methods of evaluating and filtering articles. A novel and promising approach is to examine the use and citation of articles in a new forum: Web 2.0 services like social bookmarking and microblogging. Metrics based on this data could build a “Scientometics 2.0,” supporting richer and more timely pictures of articles' impact. This paper develops the most comprehensive list of these services to date, assessing the potential value and availability of data from each. We also suggest the next steps toward building and validating metrics drawn from the social Web.},
  author = {Priem, Jason and Hemminger, Bradely H.},
  interhash = {d38dfec4da93265575aff99a811839d9},
  intrahash = {b95d32eed9419fefc007245914faad98},
  journal = {First Monday; Volume 15, Number 7 - 5 July 2010},
  title = {Scientometrics 2.0: New metrics of scholarly impact on the social Web},
  url = {http://www.uic.edu/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/2874/2570},
  year = 2010
}