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Towards a wider perspective in the social sciences using a network of variables based on thousands of results

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Abstract

This paper addresses the problem of information burying in social sciences, where a large amount of experimental findings reported in multiple scientific articles may be missed by scholars due to the lack of an active accumulation, organization and synthesis of these findings into a centralized information system. To tackle the information burying problem, in this paper we present a new network-based data model and methodology for aggregating, organizing, linking and mining quantitative results published in multiple academic articles in particular sub-fields of social sciences. The goal of the proposed methodology is to provide researchers with a wider perspective when viewing scientific results in their own fields and utilize it for their research. To validate the proposed approach, we conducted a manual experiment with a corpus of 41 scientific articles in the field of personal information management. The experiment indicates that the constructed network-based information system can be effectively used to explore the relationships between the results of various articles, raising new research questions and hypotheses based on results from multiple articles that tested similar variables. The proposed system can serve as a catalyst for the advancement of research in various fields of social science.

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Notes

  1. National Science Board (2018). Science and engineering indicators 2018. Arlington, VA: National Science Foundation. Retrieved from https://www.nsf.gov/statistics/2018/nsb20181/report/sections/academic-research-and-development/outputs-of-s-e-research-publications.

  2. National Science Board (2016). Science and engineering indicators 2016. Arlington, VA: National Science Foundation. Retrieved from https://www.nsf.gov/statistics/2016/nsb20161/uploads/1/nsb20161.pdf.

  3. Retrieved from http://www.w3.org/TR/rdf-sparql-query/.

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Acknowledgements

We thank our research assistants Natalie Friedman and Sarah Cohen for their excellent work. This study was partly supported by Google Faculty Research Award 2014_R2_79.1.

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Authors and Affiliations

  1. Department of Information Science, Bar-Ilan University, Ramat Gan, Israel

    Maayan Zhitomirsky-Geffet, Ofer Bergman & Shir Hilel

Authors
  1. Maayan Zhitomirsky-Geffet
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  2. Ofer Bergman
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  3. Shir Hilel
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Corresponding author

Correspondence to Maayan Zhitomirsky-Geffet.

Additional information

This paper is dedicated to the memory of Judit Bar-Ilan (1958–2019), an outstanding scholar and an inimitable friend and colleague.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 16 kb)

Appendix

Appendix

See Tables 1, 2, 3 and 4 and Figs. 4, 5, 6, 7, 8 and 9.

Table 1 The top-10 central variables in the corpus
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Table 2 Five largest variable community groups
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Table 3 Table of meta-analyses presenting the results of 5 meta-analyses based on a fixed model
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Table 4 Variable currently not connected to the variable ‘retrieval method’ and their hypothesized effect on it
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Fig. 4
figure 4

The variable network of “folder depth”

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Fig. 5
figure 5

A close reading of connections between variables in different articles raising new research hypotheses. The article numbers are indicated in parenthesis. Refer electronic supplementary material for references to Figs. 5–9

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Fig. 6
figure 6

A close reading of connections between variables in different articles raising new research questions

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Fig. 7
figure 7

A close reading of connections between variables in different articles allowing transformation of research techniques from one subfield to the other

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Fig. 8
figure 8

A close reading of connections between variables in different articles raising alternative explanations of results combinations

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Fig. 9
figure 9

A close reading of connections between variables in different articles suggesting parameters that can be tested when evaluating a new PIM interface

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Zhitomirsky-Geffet, M., Bergman, O. & Hilel, S. Towards a wider perspective in the social sciences using a network of variables based on thousands of results. Scientometrics 123, 1385–1406 (2020). https://doi.org/10.1007/s11192-020-03446-0

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