A Bibliometric Analysis of Deep Web Research during 1997 to 2019

Abstract

This study provides a bibliometric account of global deep web research published from 1997 to 2019. A total of 1995 records were imported from the Scopus database in a bibtex file. The bibliometrix package in RStudio was used for analyses. Publication Growth, Citations, Authorship, Country and Affiliations has been analysed. It was found that deep web research had a slow growth rate. In the last four years it has seen a recovery in the growth rate. Furthermore, this study shows the distribution of highly cited papers in the field over 23 years. It shows the country and institutional affiliation pattern of prolific authors. It also presents the most preferred sources, search terms and preferred medium of research communication. It is found that deep web research had a slow growth rate, but since 2016 it is picking up. China is the leading contributor of publications followed by the United States of America, Japan, and the United Kingdom. India is the fifth largest contributor. Contribution of citable publications has been led by Canada and USA with 81.9 per cent of efficiency followed by Australia (79.7 %), France (73.4 %) and Spain (73.1 %). It is also found that most of the prolific authors (by number of publications) do not appear in highly cited publications’ list. Deep web researchers mostly preferred using conference publications to communicate their findings. ‘Machine Learning’ and ‘cryptomarkets’ are two contemporarily popular terms being used by deep web researchers also, which indicates interest towards these topics.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 9 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.