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The relationship between citations and the linguistic traits of specific academic discourse communities identified by using social network analysis

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  • Don Watson

    (University of Bamberg)

  • Manfred Krug

    (University of Bamberg)

  • Claus-Christian Carbon

    (University of Bamberg)

Abstract

For a research article (RA) to be accepted, not only for publication, but also by its readers, it must display proficiency in the content, methodologies and discourse conventions of its specific discipline. While numerous studies have investigated the linguistic characteristics of different research disciplines, none have utilised Social Network Analysis techniques to identify communities prior to analysing their language use. This study aims to investigate the language use of three highly specific research communities in the fields of Psychology, Physics and Sports Medicine. We were interested in how these language features are related to the total number of citations, the eigencentrality within the community and the intra-network citations of the individual RAs. Applying Biber’s Multidimensional Analysis approach, a total of 771 RA abstracts published between 2010 and 2019 were analysed. We evaluated correlations between one of three network characteristics (citations, eigencentrality and in-degree), the corpora’s dimensions and 72 individual language features. The pattern of correlations suggest that features cited by other RAs within the discourse community network are in almost all cases different from those that are cited by RAs from outside the network. This finding highlights the challenges of writing for both a discipline-specific and a wider audience.

Suggested Citation

  • Don Watson & Manfred Krug & Claus-Christian Carbon, 2022. "The relationship between citations and the linguistic traits of specific academic discourse communities identified by using social network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1755-1781, April.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:4:d:10.1007_s11192-022-04287-9
    DOI: 10.1007/s11192-022-04287-9
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    References listed on IDEAS

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    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. Lakshmi Balachandran Nair & Michael Gibbert, 2016. "What makes a ‘good’ title and (how) does it matter for citations? A review and general model of article title attributes in management science," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1331-1359, June.
    3. Andrea Fronzetti Colladon & Ciriaco Andrea D’Angelo & Peter A. Gloor, 2020. "Predicting the future success of scientific publications through social network and semantic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 357-377, July.
    4. Jha, Yamini & Welch, Eric W., 2010. "Relational mechanisms governing multifaceted collaborative behavior of academic scientists in six fields of science and engineering," Research Policy, Elsevier, vol. 39(9), pages 1174-1184, November.
    5. Lu, Chao & Bu, Yi & Dong, Xianlei & Wang, Jie & Ding, Ying & Larivière, Vincent & Sugimoto, Cassidy R. & Paul, Logan & Zhang, Chengzhi, 2019. "Analyzing linguistic complexity and scientific impact," Journal of Informetrics, Elsevier, vol. 13(3), pages 817-829.
    6. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    7. Dolnicar, Sara & Chapple, Alexander, 2015. "The readability of articles in tourism journals," Annals of Tourism Research, Elsevier, vol. 52(C), pages 161-166.
    8. Didegah, Fereshteh & Thelwall, Mike, 2013. "Which factors help authors produce the highest impact research? Collaboration, journal and document properties," Journal of Informetrics, Elsevier, vol. 7(4), pages 861-873.
    9. Hamid R. Jamali & Mahsa Nikzad, 2011. "Article title type and its relation with the number of downloads and citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 653-661, August.
    10. Lei Lei & Sheng Yan, 2016. "Readability and citations in information science: evidence from abstracts and articles of four journals (2003–2012)," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1155-1169, September.
    11. George A. Barnett & Catherine Huh & Youngju Kim & Han Woo Park, 2011. "Citations among communication journals and other disciplines: a network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 449-469, August.
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