@inproceedings{kaur2014scholarometer,
  abstract = {Scholarometer (scholarometer.indiana.edu) is a social tool developed to facilitate citation analysis and help evaluate the impact of authors. The Scholarometer service allows scholars to compute various citation-based impact measures. In exchange, users provide disciplinary annotations of authors, which allow for the computation of discipline-specific statistics and discipline-neutral impact metrics. We present here two improvements of our system. First, we integrated a new universal impact metric hs that uses crowdsourced data to calculate the global rank of a scholar across disciplinary boundaries. Second, improvements made in ambiguous name classification have increased the accuracy from 80% to 87%.},
  acmid = {2615669},
  address = {New York, NY, USA},
  author = {Kaur, Jasleen and JafariAsbagh, Mohsen and Radicchi, Filippo and Menczer, Filippo},
  booktitle = {Proceedings of the 2014 ACM Conference on Web Science},
  doi = {10.1145/2615569.2615669},
  interhash = {bfb4274f2a002cde9efbe71faf295e6a},
  intrahash = {4edc2b8ed7acdd1ef8be4d6eefea8718},
  isbn = {978-1-4503-2622-3},
  location = {Bloomington, Indiana, USA},
  numpages = {2},
  pages = {285--286},
  publisher = {ACM},
  series = {WebSci '14},
  title = {Scholarometer: A System for Crowdsourcing Scholarly Impact Metrics},
  url = {http://doi.acm.org/10.1145/2615569.2615669},
  year = 2014
}

@article{li2012validating,
  abstract = {This paper investigates whether CiteULike and Mendeley are useful for measuring scholarly influence, using a sample of 1,613 papers published in Nature and Science in 2007. Traditional citation counts from the Web of Science (WoS) were used as benchmarks to compare with the number of users who bookmarked the articles in one of the two free online reference manager sites. Statistically significant correlations were found between the user counts and the corresponding WoS citation counts, suggesting that this type of influence is related in some way to traditional citation-based scholarly impact but the number of users of these systems seems to be still too small for them to challenge traditional citation indexes.},
  author = {Li, Xuemei and Thelwall, Mike and Giustini, Dean},
  doi = {10.1007/s11192-011-0580-x},
  interhash = {9f186a30dbe5af5dec8a49604bcca3dd},
  intrahash = {60c73c95336adf02c315c7b4c434cfd4},
  issn = {0138-9130},
  journal = {Scientometrics},
  language = {English},
  number = 2,
  pages = {461-471},
  publisher = {Springer Netherlands},
  title = {Validating online reference managers for scholarly impact measurement},
  url = {http://dx.doi.org/10.1007/s11192-011-0580-x},
  volume = 91,
  year = 2012
}

@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
}