Evaluating Research Trends from Journal Paper Metadata, Considering the Research Publication Latency

Abstract

Investigating the research trends within a scientific domain by analyzing semantic information extracted from scientific journals has been a topic of interest in the natural language processing (NLP) field. A research trend evaluation is generally based on the time evolution of the term occurrence or the term topic, but it neglects an important aspect—research publication latency. The average time lag between the research and its publication may vary from one month to more than one year, and it is a characteristic that may have significant impact when assessing research trends, mainly for rapidly evolving scientific areas. To cope with this problem, the present paper is the first work that explicitly considers research publication latency as a parameter in the trend evaluation process. Consequently, we provide a new trend detection methodology that mixes auto-ARIMA prediction with Mann–Kendall trend evaluations. The experimental results in an electronic design automation case study prove the viability of our approach.

Automating Research Problem Framing and Exploration through Knowledge Extraction from Bibliometric Data

“…The publication latency δ PL , also known as publication time or editorial handling time, is a specific characteristic of the publication (e.g., journal) to which the research results are submitted for review and publication. It is defined as the average time interval between the moment a manuscript is submitted and the moment the manuscript is published [8]. Since this information is not stored in bibliometric databases, it needs to be either acquired from the publication's website, if this information is available, or approximated by averaging the time needed to publish the most recent corpus of research papers.…”

“…To identify the given key term in fields 'Title,''Keyword,' and 'Abstract,' a TagMe procedure with a link-probability, lp ¼ 0:1, was utilized, following the same methodology as the one described in Ref. [8].…”

“…The unprecedented pace at which the volume of research outputs has increased not only the produced opportunities but also the difficulty of the challenges must be addressed. Some of the research outputs in digital format include books, book chapters, journal articles, conference proceedings, patents, student theses, technical reports and documents, blogs, web pages, podcasts, design descriptions, computer code, and many more [8][9][10]. Ubiquitous, fast, and often free access to research documents in digital format supports a quick dissemination of the result.…”

“…Simultaneously, the challenges of effectively managing the huge volume of research outputs have also become much harder, less accurate, and more error-prone. For example, continuously and precisely keeping track of the published results is likely impossible due to the sheer size of the results and the difficulty of summarizing it in a way that is usable by humans for analysis, reasoning, and creative problem solving [8]. Even if somebody could somehow read all publications produced on a topic, it would be arguably impossible to cognitively produce a mental representation of the work.…”

Identifying Hot Information Security Topics Using LDA and Multivariate Mann-Kendall Test

“…Relying the topic trend evaluations on citation-related indices is drastically affected by the time needed to accomplish the citing research and the publication latency [17] and is not appropriate for fast-changing domains such as Internet of Things, Machine Learning or Information Security. This leaves room to content-based NLP methods where the time series used for evaluation considers the time evolution of key terms extracted from publication content.…”