IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v18y2024i3s1751157724000397.html
   My bibliography  Save this article

Scientific impact analysis: Unraveling the link between linguistic properties and citations

Author

Listed:
  • Porwal, Priya
  • Devare, Manoj H.

Abstract

The Scholar's success is indicated by the number of citations it received for its publication. Examining the correlation between the linguistic attributes of scholarly publications and their scientific influence holds significant importance. This study analyzed 1000 research papers by highly ranked authors from computer science and electronics backgrounds. The title, abstract, and conclusion sections of the paper were analyzed. This study utilizes readability, lexical diversity, lexical density, syntactic features, and coherence measures to establish the correlation between citations and the textual content of an article. The characteristics of the publication were evaluated in relation to its research impact, which was classified into two categories, high citations and low citations. Additionally, the influence of various aspects on citations was assessed through the utilisation of the negative binomial regression model, ordinary least square model, and spearman correlation. This analysis took into account the characteristics of length and structure. The results highlight a clear positive link between abstract readability, and number of references with increased citations. Additionally, each additional page contributes to a 0.2 % increase in citation count. However, the number of diagrams and conclusion readability show no significant connection with citations. Factors like title length, abstract length, and conclusion length also exhibit associations, though with slightly lower percentages. The results indicate that linguistic characteristics exert a limited impact on the acquisition of citations.

Suggested Citation

  • Porwal, Priya & Devare, Manoj H., 2024. "Scientific impact analysis: Unraveling the link between linguistic properties and citations," Journal of Informetrics, Elsevier, vol. 18(3).
  • Handle: RePEc:eee:infome:v:18:y:2024:i:3:s1751157724000397
    DOI: 10.1016/j.joi.2024.101526
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157724000397
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2024.101526?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Natsuo Onodera & Fuyuki Yoshikane, 2015. "Factors affecting citation rates of research articles," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(4), pages 739-764, April.
    2. Bornmann, Lutz & Leydesdorff, Loet, 2015. "Does quality and content matter for citedness? A comparison with para-textual factors and over time," Journal of Informetrics, Elsevier, vol. 9(3), pages 419-429.
    3. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
    4. Saeed-Ul Hassan & Mubashir Imran & Sehrish Iqbal & Naif Radi Aljohani & Raheel Nawaz, 2018. "Deep context of citations using machine-learning models in scholarly full-text articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1645-1662, December.
    5. Anqi Ma & Yu Liu & Xiujuan Xu & Tao Dong, 2021. "A deep-learning based citation count prediction model with paper metadata semantic features," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6803-6823, August.
    6. Costas, Rodrigo & Bordons, María, 2007. "The h-index: Advantages, limitations and its relation with other bibliometric indicators at the micro level," Journal of Informetrics, Elsevier, vol. 1(3), pages 193-203.
    7. 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.
    8. Kathy McKeown & Hal Daume III & Snigdha Chaturvedi & John Paparrizos & Kapil Thadani & Pablo Barrio & Or Biran & Suvarna Bothe & Michael Collins & Kenneth R. Fleischmann & Luis Gravano & Rahul Jha & B, 2016. "Predicting the impact of scientific concepts using full-text features," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2684-2696, November.
    9. 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.
    10. Chao Lu & Yi Bu & Jie Wang & Ying Ding & Vetle Torvik & Matthew Schnaars & Chengzhi Zhang, 2019. "Examining scientific writing styles from the perspective of linguistic complexity," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(5), pages 462-475, May.
    11. Dag W Aksnes, 2003. "Characteristics of highly cited papers," Research Evaluation, Oxford University Press, vol. 12(3), pages 159-170, December.
    12. Nick Haslam & Lauren Ban & Leah Kaufmann & Stephen Loughnan & Kim Peters & Jennifer Whelan & Sam Wilson, 2008. "What makes an article influential? Predicting impact in social and personality psychology," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(1), pages 169-185, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sun, Zhuanlan & He, Dongjin & Li, Yiwei, 2024. "How the readability of manuscript before journal submission advantages peer review process: Evidence from biomedical scientific publications," Journal of Informetrics, Elsevier, vol. 18(3).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kayvan Kousha & Mike Thelwall, 2024. "Factors associating with or predicting more cited or higher quality journal articles: An Annual Review of Information Science and Technology (ARIST) paper," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 75(3), pages 215-244, March.
    2. 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.
    3. Mingyang Wang & Shi Li & Guangsheng Chen, 2017. "Detecting latent referential articles based on their vitality performance in the latest 2 years," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1557-1571, September.
    4. Peter Sjögårde & Fereshteh Didegah, 2022. "The association between topic growth and citation impact of research publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1903-1921, April.
    5. Sepideh Fahimifar & Khadijeh Mousavi & Fatemeh Mozaffari & Marcel Ausloos, 2023. "Identification of the most important external features of highly cited scholarly papers through 3 (i.e., Ridge, Lasso, and Boruta) feature selection data mining methods," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3685-3712, August.
    6. Zahedi, Zohreh & Haustein, Stefanie, 2018. "On the relationships between bibliographic characteristics of scientific documents and citation and Mendeley readership counts: A large-scale analysis of Web of Science publications," Journal of Informetrics, Elsevier, vol. 12(1), pages 191-202.
    7. Juan Xie & Kaile Gong & Ying Cheng & Qing Ke, 2019. "The correlation between paper length and citations: a meta-analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 763-786, March.
    8. Amon, Julian & Hornik, Kurt, 2022. "Is it all bafflegab? – Linguistic and meta characteristics of research articles in prestigious economics journals," Journal of Informetrics, Elsevier, vol. 16(2).
    9. Kun Sun & Haitao Liu & Wenxin Xiong, 2021. "The evolutionary pattern of language in scientific writings: A case study of Philosophical Transactions of Royal Society (1665–1869)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1695-1724, February.
    10. Wanjun Xia & Tianrui Li & Chongshou Li, 2023. "A review of scientific impact prediction: tasks, features and methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 543-585, January.
    11. Jorge A. V. Tohalino & Laura V. C. Quispe & Diego R. Amancio, 2021. "Analyzing the relationship between text features and grants productivity," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4255-4275, May.
    12. Stegehuis, Clara & Litvak, Nelly & Waltman, Ludo, 2015. "Predicting the long-term citation impact of recent publications," Journal of Informetrics, Elsevier, vol. 9(3), pages 642-657.
    13. Ruan, Xuanmin & Zhu, Yuanyang & Li, Jiang & Cheng, Ying, 2020. "Predicting the citation counts of individual papers via a BP neural network," Journal of Informetrics, Elsevier, vol. 14(3).
    14. 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.
    15. Juan Xie & Kaile Gong & Jiang Li & Qing Ke & Hyonchol Kang & Ying Cheng, 2019. "A probe into 66 factors which are possibly associated with the number of citations an article received," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1429-1454, June.
    16. Guoqiang Liang & Haiyan Hou & Xiaodan Lou & Zhigang Hu, 2019. "Qualifying threshold of “take-off” stage for successfully disseminated creative ideas," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1193-1208, September.
    17. Drahomira Herrmannova & Robert M. Patton & Petr Knoth & Christopher G. Stahl, 2018. "Do citations and readership identify seminal publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 239-262, April.
    18. Onodera, Natsuo, 2016. "Properties of an index of citation durability of an article," Journal of Informetrics, Elsevier, vol. 10(4), pages 981-1004.
    19. Hongquan Shen & Juan Xie & Jiang Li & Ying Cheng, 2021. "The correlation between scientific collaboration and citation count at the paper level: a meta-analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3443-3470, April.
    20. Akella, Akhil Pandey & Alhoori, Hamed & Kondamudi, Pavan Ravikanth & Freeman, Cole & Zhou, Haiming, 2021. "Early indicators of scientific impact: Predicting citations with altmetrics," Journal of Informetrics, Elsevier, vol. 15(2).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:18:y:2024:i:3:s1751157724000397. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.