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  • × theme_ss:"Informetrie"
  1. Vaughan, L.; Yang, R.: Web data as academic and business quality estimates : a comparison of three data sources (2012) 0.01
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  2. García, J.A.; Rodríguez-Sánchez, R.; Fdez-Valdivia, J.; Robinson-García, N.; Torres-Salinas, D.: Mapping academic institutions according to their journal publication profile : Spanish universities as a case study (2012) 0.01
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  3. Costas, R.; Leeuwen, T.N. van; Bordons, M.: Referencing patterns of individual researchers : do top scientists rely on more extensive information sources? (2012) 0.01
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  4. Visscher, A. De: What does the g-index really measure? (2011) 0.01
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    Abstract
    It was argued recently that the g-index is a measure of a researcher's specific impact (i.e., impact per paper) as much as it is a measure of overall impact. While this is true for the productive "core" of publications, it can be argued that the g-index does not differ from the square root of the total number of citations in a bibliometrically meaningful way when the entire publication list is considered. The R-index also has a tendency to follow total impact, leaving only the A-index as a true measure of specific impact. The main difference between the g-index and the h-index is that the former penalizes consistency of impact whereas the latter rewards such consistency. It is concluded that the h-index is a better bibliometric tool than is the g-index, and that the square root of the total number of citations is a convenient measure of a researcher's overall impact.
  5. Li, R.; Chambers, T.; Ding, Y.; Zhang, G.; Meng, L.: Patent citation analysis : calculating science linkage based on citing motivation (2014) 0.01
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  6. Mena-Chalco, J.P.; Digiampietri, L.A.; Fabrício Martins Lopes, F.; Marcondes Cesar Junior, R.: Brazilian bibliometric coauthorship networks (2014) 0.01
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  7. Zuccala, A.; Someren, M. van; Bellen, M. van: ¬A machine-learning approach to coding book reviews as quality indicators : toward a theory of megacitation (2014) 0.01
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    Abstract
    A theory of "megacitation" is introduced and used in an experiment to demonstrate how a qualitative scholarly book review can be converted into a weighted bibliometric indicator. We employ a manual human-coding approach to classify book reviews in the field of history based on reviewers' assessments of a book author's scholarly credibility (SC) and writing style (WS). In total, 100 book reviews were selected from the American Historical Review and coded for their positive/negative valence on these two dimensions. Most were coded as positive (68% for SC and 47% for WS), and there was also a small positive correlation between SC and WS (r = 0.2). We then constructed a classifier, combining both manual design and machine learning, to categorize sentiment-based sentences in history book reviews. The machine classifier produced a matched accuracy (matched to the human coding) of approximately 75% for SC and 64% for WS. WS was found to be more difficult to classify by machine than SC because of the reviewers' use of more subtle language. With further training data, a machine-learning approach could be useful for automatically classifying a large number of history book reviews at once. Weighted megacitations can be especially valuable if they are used in conjunction with regular book/journal citations, and "libcitations" (i.e., library holding counts) for a comprehensive assessment of a book/monograph's scholarly impact.
  8. García, J.A.; Rodriguez-Sánchez, R.; Fdez-Valdivia, J.: Social impact of scholarly articles in a citation network (2015) 0.01
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  9. Orduña-Malea, E.; Torres-Salinas, D.; López-Cózar, E.D.: Hyperlinks embedded in twitter as a proxy for total external in-links to international university websites (2015) 0.01
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    Abstract
    Twitter as a potential alternative source of external links for use in webometric analysis is analyzed because of its capacity to embed hyperlinks in different tweets. Given the limitations on searching Twitter's public application programming interface (API), we used the Topsy search engine as a source for compiling tweets. To this end, we took a global sample of 200 universities and compiled all the tweets with hyperlinks to any of these institutions. Further link data was obtained from alternative sources (MajesticSEO and OpenSiteExplorer) in order to compare the results. Thereafter, various statistical tests were performed to determine the correlation between the indicators and the possibility of predicting external links from the collected tweets. The results indicate a high volume of tweets, although they are skewed by the performance of specific universities and countries. The data provided by Topsy correlated significantly with all link indicators, particularly with OpenSiteExplorer (r?=?0.769). Finally, prediction models do not provide optimum results because of high error rates. We conclude that the use of Twitter (via Topsy) as a source of hyperlinks to universities produces promising results due to its high correlation with link indicators, though limited by policies and culture regarding use and presence in social networks.
  10. Costas, R.; Zahedi, Z.; Wouters, P.: Do "altmetrics" correlate with citations? : extensive comparison of altmetric indicators with citations from a multidisciplinary perspective (2015) 0.01
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  11. Bornmann, L.; Mutz, R.: Growth rates of modern science : a bibliometric analysis based on the number of publications and cited references (2015) 0.01
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  12. Amolochitis, E.; Christou, I.T.; Tan, Z.-H.; Prasad, R.: ¬A heuristic hierarchical scheme for academic search and retrieval (2013) 0.01
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  13. Zhang, L.; Rousseau, R.; Glänzel, W.: Diversity of references as an indicator of the interdisciplinarity of journals : taking similarity between subject fields into account (2016) 0.01
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  14. Ibáñez, A.; Armañanzas, R.; Bielza, C.; Larrañaga, P.: Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices (2016) 0.01
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  15. McKeown, K.; Daume III, H.; Chaturvedi, S.; Paparrizos, J.; Thadani, K.; Barrio, P.; Biran, O.; Bothe, S.; Collins, M.; Fleischmann, K.R.; Gravano, L.; Jha, R.; King, B.; McInerney, K.; Moon, T.; Neelakantan, A.; O'Seaghdha, D.; Radev, D.; Templeton, C.; Teufel, S.: Predicting the impact of scientific concepts using full-text features (2016) 0.01
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  16. Bornmann, L.; Haunschild, R.: ¬An empirical look at the nature index (2017) 0.01
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  17. Schneider, J.W.; Costas, R.: Identifying potential "breakthrough" publications using refined citation analyses : three related explorative approaches (2017) 0.01
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  18. Botting, N.; Dipper, L.; Hilari, K.: ¬The effect of social media promotion on academic article uptake (2017) 0.01
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    Abstract
    Important emerging measures of academic impact are article download and citation rates. Yet little is known about the influences on these and ways in which academics might manage this approach to dissemination. Three groups of papers by academics in a center for speech-language-science (available through a university repository) were compared. The first group of target papers were blogged, and the blogs were systematically tweeted. The second group of connected control papers were nonblogged papers that we carefully matched for author, topic, and year of publication. The third group were papers by different staff members on a variety of topics-Unrelated Control Papers. The results suggest an effect of social media on download rate, which was limited not just to Target Papers but also generalized to Connected Control Papers. Unrelated Control Papers showed no increase over the same amount of time (main effect of time, F(1,27)?=?55.6, p?<?.001); Significant Group×Time Interaction, F(2,27)?=?7.9, p?=?.002). The effect on citation rates was less clear but followed the same trend. The only predictor of the 2015 citation rate was downloads after blogging (r?=?0.450, p?=?.012). These preliminary results suggest that promotion of academic articles via social media may enhance download and citation rate and that this has implications for impact strategies.
  19. Mutz, R.; Wolbring, T.; Daniel, H.-D.: ¬The effect of the "very important paper" (VIP) designation in Angewandte Chemie International Edition on citation impact : a propensity score matching analysis (2017) 0.01
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  20. Farys, R.; Wolbring, T.: Matched control groups for modeling events in citation data : an illustration of nobel prize effects in citation networks (2017) 0.01
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