Search (22 results, page 1 of 2)

  • × year_i:[2020 TO 2030}
  • × theme_ss:"Informetrie"
  1. Asubiaro, T.V.; Onaolapo, S.: ¬A comparative study of the coverage of African journals in Web of Science, Scopus, and CrossRef (2023) 0.03
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    Date
    22. 6.2023 14:09:06
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.745-758
  2. Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023) 0.03
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    Date
    22. 6.2023 18:07:12
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.775-790
  3. Thelwall, M.; Kousha, K.; Abdoli, M.; Stuart, E.; Makita, M.; Wilson, P.; Levitt, J.: Why are coauthored academic articles more cited : higher quality or larger audience? (2023) 0.03
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    Date
    22. 6.2023 18:11:50
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.791-810
  4. Vakkari, P.; Järvelin, K.; Chang, Y.-W.: ¬The association of disciplinary background with the evolution of topics and methods in Library and Information Science research 1995-2015 (2023) 0.03
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    Date
    22. 6.2023 18:15:06
    Source
    Journal of the Association for Information Science and Technology. 74(2023) no.7, S.811-827
  5. Dederke, J.; Hirschmann, B.; Johann, D.: ¬Der Data Citation Index von Clarivate : Eine wertvolle Ressource für die Forschung und für Bibliotheken? (2022) 0.02
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    Date
    7. 9.2015 18:35:21
    7. 4.2022 13:31:51
  6. Dorsch, I.; Haustein, S.: Bibliometrie (2023) 0.01
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    Source
    Grundlagen der Informationswissenschaft. Hrsg.: Rainer Kuhlen, Dirk Lewandowski, Wolfgang Semar und Christa Womser-Hacker. 7., völlig neu gefasste Ausg
  7. Manley, S.: Letters to the editor and the race for publication metrics (2022) 0.01
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    Date
    6. 4.2022 19:22:26
  8. Lorentzen, D.G.: Bridging polarised Twitter discussions : the interactions of the users in the middle (2021) 0.01
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    Date
    20. 1.2015 18:30:22
  9. Milard, B.; Pitarch, Y.: Egocentric cocitation networks and scientific papers destinies (2023) 0.01
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    Date
    21. 3.2023 19:22:14
  10. Tay, W.; Zhang, X.; Karimi , S.: Beyond mean rating : probabilistic aggregation of star ratings based on helpfulness (2020) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 71(2020) no.7, S.784-799
  11. Gök, A.; Karaulova, M.: How "international" is international research collaboration? (2024) 0.01
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    Date
    7. 1.2024 17:43:45
  12. Liu, X.; Bu, Y.; Li, M.; Li, J.: Monodisciplinary collaboration disrupts science more than multidisciplinary collaboration (2024) 0.01
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    Date
    7. 1.2024 18:28:33
  13. Thelwall, M.; Thelwall, S.: ¬A thematic analysis of highly retweeted early COVID-19 tweets : consensus, information, dissent and lockdown life (2020) 0.01
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    Date
    20. 1.2015 18:30:22
  14. Wang, S.; Ma, Y.; Mao, J.; Bai, Y.; Liang, Z.; Li, G.: Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities : On the rise of scrape-and-report scholarship in online reviews research (2023) 0.01
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    Date
    22. 1.2023 18:37:33
  15. Cerda-Cosme, R.; Méndez, E.: Analysis of shared research data in Spanish scientific papers about COVID-19 : a first approach (2023) 0.01
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    Date
    21. 3.2023 19:22:02
  16. Fang, Z.; Costas, R.; Tian, W.; Wang, X.; Wouters, P.: How is science clicked on Twitter? : click metrics for Bitly short links to scientific publications (2021) 0.01
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.7, S.918-932
  17. Ikae, C.; Savoy, J.: Gender identification on Twitter (2022) 0.01
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    Abstract
    To determine the author of a text's gender, various feature types have been suggested (e.g., function words, n-gram of letters, etc.) leading to a huge number of stylistic markers. To determine the target category, different machine learning models have been suggested (e.g., logistic regression, decision tree, k nearest-neighbors, support vector machine, naïve Bayes, neural networks, and random forest). In this study, our first objective is to know whether or not the same model always proposes the best effectiveness when considering similar corpora under the same conditions. Thus, based on 7 CLEF-PAN collections, this study analyzes the effectiveness of 10 different classifiers. Our second aim is to propose a 2-stage feature selection to reduce the feature size to a few hundred terms without any significant change in the performance level compared to approaches using all the attributes (increase of around 5% after applying the proposed feature selection). Based on our experiments, neural network or random forest tend, on average, to produce the highest effectiveness. Moreover, empirical evidence indicates that reducing the feature set size to around 300 without penalizing the effectiveness is possible. Finally, based on such reduced feature sizes, an analysis reveals some of the specific terms that clearly discriminate between the 2 genders.
  18. Järvelin, K.; Vakkari, P.: LIS research across 50 years: content analysis of journal articles : offering an information-centric conception of memes (2022) 0.01
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    Source
    Journal of documentation. 78(2022) no.7, S.65-88
  19. Tian, W.; Cai, R.; Fang, Z.; Geng, Y.; Wang, X.; Hu, Z.: Understanding co-corresponding authorship : a bibliometric analysis and detailed overview (2024) 0.01
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    Date
    7. 1.2024 17:47:11
  20. Delgado-Quirós, L.; Aguillo, I.F.; Martín-Martín, A.; López-Cózar, E.D.; Orduña-Malea, E.; Ortega, J.L.: Why are these publications missing? : uncovering the reasons behind the exclusion of documents in free-access scholarly databases (2024) 0.01
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    Date
    7. 1.2024 18:13:38