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Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series

Author

Listed:
  • Haiyun Xu

    (Chengdu Library of Chinese Academy of Sciences)

  • Ting Guo

    (Chengdu Library of Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Zenghui Yue

    (Jining Medical University)

  • Lijie Ru

    (Chengdu Library of Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Shu Fang

    (Chengdu Library of Chinese Academy of Sciences)

Abstract

Interdisciplinarity is increasingly widespread. Many technological frontiers and hotspots are emerging in the intersecting research areas. The existing measurement indexes of interdisciplinarity are mostly based on the co-occurrence of authors, institutions, or references, and most focus on the tendency to interdisciplinarity. This paper introduces a new measurement index entitled topic terms interdisciplinarity (TI) for interdisciplinarity topic mining. Taking Information Science & Library Science (LIS) as a case study, this paper identifies interdisciplinary topics by calculating TI values together with Bet values, term frequency values, and others, and analyzes the evolution of interdisciplinary sciences based on social network analysis and time series analysis. It was found that the intersections of external disciplines and pivots of internal topics for LIS can be identified by the utilization of TI value and Bet values. The research has shown that the TI value can identify interdisciplinary topic terms well, and it will be an efficient indicator for interdisciplinary analysis by being complementary to other methods.

Suggested Citation

  • Haiyun Xu & Ting Guo & Zenghui Yue & Lijie Ru & Shu Fang, 2016. "Interdisciplinary topics of information science: a study based on the terms interdisciplinarity index series," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 583-601, February.
    • Handle: RePEc:spr:scient:v:106:y:2016:i:2:d:10.1007_s11192-015-1792-2
    DOI: 10.1007/s11192-015-1792-2
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    References listed on IDEAS

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    3. 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.
    4. Kun Dong & Haiyun Xu & Rui Luo & Ling Wei & Shu Fang, 2018. "An integrated method for interdisciplinary topic identification and prediction: a case study on information science and library science," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 849-868, May.
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    6. Xie, Qing & Zhang, Xinyuan & Ding, Ying & Song, Min, 2020. "Monolingual and multilingual topic analysis using LDA and BERT embeddings," Journal of Informetrics, Elsevier, vol. 14(3).
    7. Shengli Deng & Sudi Xia, 2020. "Mapping the interdisciplinarity in information behavior research: a quantitative study using diversity measure and co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 489-513, July.
    8. Manika Lamba & Margam Madhusudhan, 2019. "Mapping of topics in DESIDOC Journal of Library and Information Technology, India: a study," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 477-505, August.
    9. Kim, Hyeyoung & Park, Hyelin & Song, Min, 2022. "Developing a topic-driven method for interdisciplinarity analysis," Journal of Informetrics, Elsevier, vol. 16(2).
    10. Wang, Shiyun & Mao, Jin & Lu, Kun & Cao, Yujie & Li, Gang, 2021. "Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth," Journal of Informetrics, Elsevier, vol. 15(4).
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