Computational social science for nonprofit studies: Developing a toolbox and knowledge base for the field

Ji Ma*, Islam Akef Ebeid, Arjen de Wit, Meiying Xu, Yongzheng Yang, René Bekkers, Pamala Wiepking

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


How can computational social science (CSS) methods be applied in nonprofit and philanthropic studies? This paper summarizes and explains a range of relevant CSS methods from a research design perspective and highlights key applications in our field. We define CSS as a set of computationally intensive empirical methods for data management, concept representation, data analysis, and visualization. What makes the computational methods “social” is that the purpose of using these methods is to serve quantitative, qualitative, and mixed-methods social science research, such that theorization can have a solid ground. We illustrate the promise of CSS in our field by using it to construct the largest and most comprehensive database of scholarly references in our field, the Knowledge Infrastructure of Nonprofit and Philanthropic Studies (KINPS). Furthermore, we show that through the application of CSS in constructing and analyzing KINPS, we can better understand and facilitate the intellectual growth of our field. We conclude the article with cautions for using CSS and suggestions for future studies implementing CSS and KINPS.

Original languageEnglish
Pages (from-to)52-63
Issue number1
Early online date2021
Publication statusPublished - Feb 2023

Bibliographical note

Funding Information:
The authors thank the Revolutionizing Philanthropy Research Consortium for suggestions on keywords, Sasha Zarins for organizing the Consortium, and Gary King for commenting on interdisciplinary collaboration. We appreciate the constructive comments from Mirae Kim, Paloma Raggo, and the anonymous reviewers, and thank Taco Brandsen and Susan Appe for handling the manuscript. The development of KINPS uses the Chameleon testbed supported by the U.S. National Science Foundation.

Funding Information:
J.M. was supported in part by the 2019–20 PRI Award and Stephen H. Spurr Centennial Fellowship from the LBJ School of Public Affairs and the Academic Development Funds from the RGK Center. P.W.’s work at the IU Lilly Family School of Philanthropy is funded through a donation by the Stead Family; her work at the VU University Amsterdam is funded by the Dutch Charity Lotteries.

Publisher Copyright:
© 2021, The Author(s).

Copyright 2021 Elsevier B.V., All rights reserved.


  • Computational social science
  • Knowledge Infrastructure of Nonprofit and Philanthropic Studies
  • Nonprofit
  • Philanthropy


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