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Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches

Authors:

Dietmar WolframAuthors Info & Claims

Journal of the American Society for Information Science and Technology, Volume 63, Issue 10

Pages 1973 - 1986

https://doi.org/10.1002/asi.22628

Published: 01 October 2012 Publication History

Abstract

Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map. © 2012 Wiley Periodicals, Inc.

References

[1]

Ahlgren, P., & Colliander, C. (2009). Document-document similarity approaches and science mapping: Experimental comparison of five approaches. Journal of Informetrics, 3(1), 49–63.

[2]

Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson's correlation coefficient. Journal of the American Society for Information Science and Technology, 54(6), 550–560.

Digital Library

[3]

Blei, D.M., Ng, A.Y., & Jordan, M.J. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993––1022.

Digital Library

[4]

Börner, K., Chen, C., & Boyack, K.W. (2005). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37(1), 179––255.

[5]

Bornmann, L., & Daniel, H-D. (2008). What do citation counts measure? A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.

[6]

Boyack, K.W., & Klavans, R. (2010). Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately? Journal of the American Society for Information Science and Technology, 61(12), 2389–2404.

Digital Library

[7]

Braam, R.R., Moed, H.F., & van Raan, A.F.J. (1991). Mapping of science by combined co-citation and word analysis. I. Structural aspects. Journal of the American Society for Information Science, 42(4), 233–251.

[8]

Cao, M.D., & Gao, X. (2005). Combining contents and citations for scientific document classification. Lecture Notes in Computer Science, 3809, 143–152.

Digital Library

[9]

Chang, J., Gerrish, S., Wang, C., & Blei, D.M. (2009). Reading tea leaves: How humans interpret topic models. Computer and Information Science, 31, 1––9.

[10]

Coulter, N., Monarch, I., & Konda, S. (1998). Software engineering as seen through its research literature: A study in co-word analysis. Journal of the American Society for Information Science, 49(13), 1206–1223.

[11]

Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391–407.

[12]

Ding, Y. (2011a). Topic-based PageRank on author cocitation networks. Journal of the American Society for Information Science and Technology, 62(3), 449–466.

Digital Library

[13]

Ding, Y. (2011b). Scientific collaboration and endorsement: Network analysis of coauthorship and citation networks. Journal of Informetrics, 5(1), 187–203.

[14]

Ding, Y., Chowdhury, G.G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management, 37(6), 817––842.

Digital Library

[15]

Glänzel, W. (2001). National characteristics in international scientific co-authorship relations. Scientometrics, 51(1), 69–115.

[16]

Griffiths, T.L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101, 5228–5235.

[17]

Healey, P., Rothman, H., & Hoch, P.K. (1986). An experiment in science mapping for research planning. Research Policy, 15(5), 233––251.

[18]

Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR'1999) (pp. 50–57). Berkeley, CA: ACM.

[19]

Kessler, M.M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25.

[20]

Law, J., & Whittaker, J. (1992). Mapping acidification research: A test of the co-word method. Scientometrics, 23(3), 417––461.

[21]

Leydesdorff, L. (1987). Various methods for the mapping of science. Scientometrics, 11(5-6), 295–324.

[22]

Leydesdorff, L. (1997). Why words and co-words cannot map the development of the sciences. Journal of the American Society for Information Science, 48(5), 418–427.

[23]

Leydesdorff, L., & Vaughan, L. (2006). Co-occurrence matrices and their applications in information science: Extending ACA to the web environment. Journal of the American Society for Information Science and Technology, 57(12), 1616–1628.

Digital Library

[24]

Liu, X., Bollen, J., Nelson, M.L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing & Management, 41(6), 1462––1480.

[25]

Liu, X., Yu, S., Janssens, F., Glänzel, W., Moreau, Y., & Moor, B.D. (2010). Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database. Journal of the American Society for Information Science and Technology, 61(6), 1105–1119.

[26]

Ponte, J., & Croft, W.B. (1998). A language modeling approach to information retrieval. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR'1998) (pp. 275–281). Melbourne, Australia: ACM.

[27]

Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., & Steyvers, M. (2010). Learning author-topic models from text corpora. ACM Transactions on Information Systems, 28(1), 1––38.

Digital Library

[28]

Salton, G., & McGill, M.J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.

[29]

Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28(11), 758–775.

[30]

Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.

[31]

Sugimoto, C.R., Li, D., Russell, T.G. Finlay, C.S., & Ding, Y. (2011). The shifting sands of disciplinary development: Analyzing North American Library and Information Science dissertations using latent Dirichlet allocation. Journal of the American Society for Information Science and Technology, 62(1), 185–204.

Digital Library

[32]

van Eck, N.J., & Waltman, L. (2009). How to normalize cooccurrence data? An analysis of some well-known similarity measures. Journal of the American Society for Information Science and Technology, 60(8), 1635–1651.

[33]

Weinberg, B.H. (1974). Bibliographic coupling: A review. Information Storage and Retrieval, 10(5-6), 189–196.

[34]

White, H.D. (2003). Author cocitation analysis and Pearson's r. Journal of the American Society for Information Science and Technology, 54(13), 1250–1259.

Digital Library

[35]

White, H.D., & Griffith, B.C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163–171.

[36]

Zhao, D., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in Information Science 1996-2005: Introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070–2086.

Digital Library

[37]

Zhao, D., & Strotmann, A. (2010). Mapping the highly collaborative Stem Cell research field: Adding last-author-based analysis to author co-citation analysis family. In Proceedings of the American Society for Information Science and Technology, Pittsburgh, PA: ASI.

[38]

Zhao, D., & Strotmann, A. (2011). Counting first, last, or all authors in citation analysis: A comprehensive comparison in the highly collaborative stem cell research field. Journal of the American Society for Information Science and Technology, 62(4), 654–676.

Digital Library

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Lyu XCostas R(2024)Studying the cognitive relatedness between topics in the global science landscapeJournal of Information Science10.1177/0165551522112197050:6(1429-1448)Online publication date: 1-Dec-2024
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cover image Journal of the American Society for Information Science and Technology

Journal of the American Society for Information Science and Technology Volume 63, Issue 10

October 2012

225 pages

ISSN:1532-2882

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Publisher

John Wiley & Sons, Inc.

United States

Publication History

Published: 01 October 2012

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Cited By

Lyu XCostas R(2024)Studying the cognitive relatedness between topics in the global science landscapeJournal of Information Science10.1177/0165551522112197050:6(1429-1448)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1177/01655515221121970
Song YLei L(2024)A comparative analysis of Inventor Patent Classification Coupling between the first-inventor and all-inventorJournal of Information Science10.1177/0165551522109236650:2(378-393)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1177/01655515221092366
Thakuria ADeka D(2024)A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approachScientometrics10.1007/s11192-024-05058-4129:7(3841-3869)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s11192-024-05058-4
Huang LCai YZhao EZhang SShu YFan J(2022)Measuring the interdisciplinarity of Information and Library Science interactions using citation analysis and semantic analysisScientometrics10.1007/s11192-022-04401-x127:11(6733-6761)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s11192-022-04401-x
Bonaccorsi AMelluso NMassucci F(2022)Exploring the antecedents of interdisciplinarity at the European Research Council: a topic modeling approachScientometrics10.1007/s11192-022-04368-9127:12(6961-6991)Online publication date: 1-Dec-2022
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Chauhan UShah A(2021)Topic Modeling Using Latent Dirichlet allocationACM Computing Surveys10.1145/346247854:7(1-35)Online publication date: 17-Sep-2021
https://dl.acm.org/doi/10.1145/3462478
Chauhan UShah A(2021)Improving Semantic Coherence of Gujarati Text Topic Model Using Inflectional Forms Reduction and Single-letter Words RemovalACM Transactions on Asian and Low-Resource Language Information Processing10.1145/344776020:1(1-18)Online publication date: 10-Mar-2021
https://dl.acm.org/doi/10.1145/3447760
Liang ZLiu FMao JLu K(2021)A Knowledge Representation Model for Studying Knowledge Creation, Usage, and EvolutionDiversity, Divergence, Dialogue10.1007/978-3-030-71292-1_9(97-111)Online publication date: 17-Mar-2021
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Miyata YIshita EYang FYamamoto MIwase AKurata K(2020)Knowledge structure transition in library and information science: topic modeling and visualizationScientometrics10.1007/s11192-020-03657-5125:1(665-687)Online publication date: 1-Oct-2020
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Ranaei SSuominen APorter ACarley S(2020)Evaluating technological emergence using text analytics: two case technologies and three approachesScientometrics10.1007/s11192-019-03275-w122:1(215-247)Online publication date: 1-Jan-2020
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