درخت حوزه‌های تخصصی

The present research aimed to investigate gender-specific charactonyms in the Persian Translation of John Bunyan’s (1684) The Pilgrim's Progress, Part 2. It also aimed to reveal whether the TL preferred equivalents for the SL gender-specific charactonyms could convey the meanings and the characteristics of the names in the Persian translation. The material was borrowed from the book Seir-o Solouk-i Za’ir, Bakhshe Dovom (‘سیر و سلوک زائر، بخش دوّم’) translated by Golnaz Hamedi in 2002 (1381), and also its French and Arabic translations. Thus, SL gender-specific charactonyms were compared with TL equivalents in Evelyn Hatch’s (1992) macro-level analysis. According to Hatch (1992), characters, as one of the main elements of a story, play a significant role and affect the macrostructure of the story. The results showed that the Persian equivalents used by Hamedi (2002) ranged from transparent to problematic ones. Hamedi (2002) less preserved the meanings of the names and sometimes replaced the female charactonyms with already-used male charactonyms that affected the novel due to the change of the characteristics and additional information the charactonyms bear in their inner forms.

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.