@article{masbleda2014highly,
  abstract = {Academics can now use the web and the social websites to disseminate scholarly information in a variety of different ways. Although some scholars have taken advantage of these new online opportunities, it is not clear how widespread their uptake is or how much impact they can have. This study assesses the extent to which successful scientists have social web presences, focusing on one influential group: highly cited researchers working at European institutions. It also assesses the impact of these presences. We manually and systematically identified if the European highly cited researchers had profiles in Google Scholar, Microsoft Academic Search, Mendeley, Academia and LinkedIn or any content in SlideShare. We then used URL mentions and altmetric indicators to assess the impact of the web presences found. Although most of the scientists had an institutional website of some kind, few had created a profile in any social website investigated, and LinkedIn—the only non-academic site in the list—was the most popular. Scientists having one kind of social web profile were more likely to have another in many cases, especially in the life sciences and engineering. In most cases it was possible to estimate the relative impact of the profiles using a readily available statistic and there were disciplinary differences in the impact of the different kinds of profiles. Most social web profiles had some evidence of uptake, if not impact; nevertheless, the value of the indicators used is unclear.},
  author = {Mas-Bleda, Amalia and Thelwall, Mike and Kousha, Kayvan and Aguillo, IsidroF.},
  doi = {10.1007/s11192-014-1345-0},
  interhash = {5110401b47f90128cbe885cf441ab7fb},
  intrahash = {9fa40f587b142513785037b67040abe4},
  issn = {0138-9130},
  journal = {Scientometrics},
  language = {English},
  number = 1,
  pages = {337-356},
  publisher = {Springer Netherlands},
  title = {Do highly cited researchers successfully use the social web?},
  url = {http://dx.doi.org/10.1007/s11192-014-1345-0},
  volume = 101,
  year = 2014
}

@inproceedings{saeed2008citation,
  abstract = {New developments in the collaborative and participatory role of Web has emerged new web based fast lane information systems like tagging and bookmarking applications. Same authors have shown elsewhere, that for same papers tags and bookmarks appear and gain volume very quickly in time as compared to citations and also hold good correlation with the citations. Studying the rank prediction models based on these systems gives advantage of gaining quick insight and localizing the highly productive and diffusible knowledge very early in time. This shows that it may be interesting to model the citation rank of a paper within the scope of a conference or journal issue, based on the bookmark counts (i-e count representing how many researchers have shown interest in a publication.) We used linear regression model for predicting citation ranks and compared both predicted citation rank models of bookmark counts and coauthor network counts for the papers of WWW06 conference. The results show that the rank prediction model based on bookmark counts is far better than the one based on coauthor network with mean absolute error for the first limited to the range of 5 and mean absolute error for second model above 18. Along with this we also compared the two bookmark prediction models out of which one was based on total citations rank as a dependent variable and the other was based on the adjusted citation rank. The citation rank was adjusted after subtracting the self and coauthor citations from total citations. The comparison reveals a significant improvement in the model and correlation after adjusting the citation rank. This may be interpreted that the bookmarking mechanisms represents the phenomenon similar to global discovery of a publication. While in the coauthor nets the papers are communicated personally and this communication or selection may not be captured within the bookmarking systems.},
  author = {Saeed, A.U. and Afzal, M.T. and Latif, A. and Tochtermann, K.},
  booktitle = {Multitopic Conference, 2008. INMIC 2008. IEEE International},
  doi = {10.1109/INMIC.2008.4777769},
  interhash = {26d1785cab132d577e377bb5bf299002},
  intrahash = {677fc89fef6c79a6a4f25cb25246e38a},
  month = dec,
  pages = {392-397},
  title = {Citation rank prediction based on bookmark counts: Exploratory case study of WWW06 papers},
  url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4777769},
  year = 2008
}

@article{haustein2011applying,
  abstract = {Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in \{STM\} and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content. },
  author = {Haustein, Stefanie and Siebenlist, Tobias},
  doi = {http://dx.doi.org/10.1016/j.joi.2011.04.002},
  interhash = {13fe59aae3d6ef95b529ffe00ede4126},
  intrahash = {c3e49ee7b0ed81ecd126d3ef76d5f407},
  issn = {1751-1577},
  journal = {Journal of Informetrics },
  number = 3,
  pages = {446 - 457},
  title = {Applying social bookmarking data to evaluate journal usage },
  url = {http://www.sciencedirect.com/science/article/pii/S1751157711000393},
  volume = 5,
  year = 2011
}