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Reviewing literature with social network analysis
This page presents an overview of references, techniques and tools use to create systematic review of the literature using social network analysis.
Literature reviews are required at early stages of a traditional research progression. Many systematic approaches help researchers identify relevant literature. However, there is far less support for interpreting large collections of references. Understanding the evolution of knowledge within a discipline requires an awareness of the collaborative networks from which significant advances originate. Unfortunately, this relational awareness is only acquired after years of professional experience. Therefore, early-career researchers are at a fundamental disadvantage when conducting literature reviews. Social Network Analysis (SNA) can make the interpretation of large collections of literature more manageable. This paper provides a method for generating a co-authorship network from reference lists. The partition of the co-authorship network into communities provides a reviewer with direction to organise literature into meaningful groups. The interpretation of seemingly disparate and entangled research programs is far more manageable when interpreting smaller collections of highly relevant literature.
Thomas Cowhitt, Timothy Butler & Elaine Wilson (2020) Using social network analysis to complete literature reviews: a new systematic approach for independent researchers to detect and interpret prominent research programs within large collections of relevant literature, International Journal of Social Research Methodology, 23:5, 483-496, DOI: 10.1080/13645579.2019.1704356
Link to paper: https://doi.org/10.1080/13645579.2019.1704356
Whether we are in the process of designing a new empirical study or our interest lies in conducting a review study, a solid literature review is needed to acquire an accurate idea of the current state of affairs with regard to a phenomenon of interest. Even if we can find contributions to the literature by entering keywords in search engines, we need tools that can help us to structure all the contributions encountered in terms of their interrelations and impact. This article presents social network analysis as such a tool. Although social network analysis is commonly thought of as a method in a particular empirical study, where individuals and groups of participants are studied, we can view writing and citation behavior in a field as an empirical study as well. In that context, participants can be individual authors and author teams as well as publications. Social network analysis can provide indicators that can help to qualify and quantify impact of contributions to a field across time.
Keywords: Social network analysis; Systematic review; Citation; Coauthoring; Impact
Jimmie Leppink, Patricia Pérez-Fuster (2019). ”Social Networks as an Approach to Systematic review”. In Health Professions Education, Volume 5, Issue 3. Pp. 218-224. ISSN 2452-3011, DOI: 10.1016/j.hpe.2018.09.002
Link to paper https://doi.org/10.1016/j.hpe.2018.09.002
This study introduces a method that uses a network approach towards literature review. To employ this approach, we use hypotheses proposed in scientific publications as building blocks. In network terms, a hypothesis is a directed tie between two concepts or nodes. The network emerges by aggregating the hypotheses from a set of articles in a specific domain. This study explains the method and its potential for reviewing literature in a particular domain. As a proof of concept, we provide a case study reviewing the research literature on the adoption of eGovernment services. Our analyses show that a network approach towards literature review provides novel insights into the current state of a research domain. Although there are limitations, this approach has the potential to help scholarly communities focus their research and formulate new research questions.
van de Wijngaert, L., Bouwman, H. & Contractor, N. A network approach toward literature review. Qual Quant 48, 623–643 (2014). DOI: 10.1007/s11135-012-9791-3
Link to paper https://doi.org/10.1007/s11135-012-9791-3