Analyzing Social Engineering Research through Co-authorship Networks Using Scopus Database during 1926-2020

Khalili, Leila and Darshani Wijayasundara, Nayana Analyzing Social Engineering Research through Co-authorship Networks Using Scopus Database during 1926-2020. International Journal of Digital Content Management, 2022, vol. 3, n. 4. [Journal article (Unpaginated)]

Preview

Text DCM_Volume 3_Issue 4_Pages 7-38.pdf Download (1MB) | Preview

English abstract

Purpose: Hacking the human brain and manipulating human trust to obtain information and get monetary gains is called social engineering. This study aims to visualize and analyze the co-authorship networks in the Scopus citation database's social engineering research from 1926 to 2020. Method: The present quantitative study used the bibliometric method and social network analysis. The study collected data from the Scopus database. A total number of 1994 records were taken as the sample of the study. Researchers used descriptive and inferential statistics and social network analysis to obtain results; to do this, different software types were used in the study (SPSS, Microsoft Excel, Text Statistics Analyzer, ISI.exe, Pajek, and VOSviewer). Findings: The findings indicate the top three sources of publishing and the related subject areas. Furthermore, the top three core authors and countries were identified. Also, the authors with high centrality measures in the co-authorship networks were identified. A large majority of papers had only one author. The Collaborative Coefficient among researchers was 0.36. Based on the results of Spearman's test, there was a significant association between the number of documents, the number of citations, and the rate of total link strength of the countries. Likewise, there was a positive and high significant association between degree and closeness centralities. Conclusion: The researchers' frequently used keywords in this area were social engineering, phishing, and information security; in addition, the frequency of keywords was not compatible with Zipf’s Law. A small sample of keywords will not properly follow Zipf’s distribution.

Item type:	Journal article (Unpaginated)
Keywords:	Bibliometric Co-authorship Networks Centrality Measures Social Engineering Zipf’s Law
Subjects:	H. Information sources, supports, channels. H. Information sources, supports, channels. > HL. Databases and database Networking.
Depositing user:	Mr Saeed Asgharzadeh
Date deposited:	07 Nov 2023 07:34
Last modified:	07 Nov 2023 07:34
URI:	http://hdl.handle.net/10760/45055

Downloads

Actions (login required)

View Item