Paper Info
Title
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends
Abstract
Early detection of emerging research trends could potentially revolutionise the way research is done. For this reason, trend analysis has become an area of paramount importance in academia and industry. This is due to the significant implications for research funding and public policy. The literature presents several emerging approaches to detecting new research trends. Most of these approaches rely mainly on citation counting. While citations have been widely used as indicators of emerging research topics, they suffer from some limitations. For instance, citations can take months to years to progress and then to reveal trends. Furthermore, they fail to dig into paper content. To overcome this problem, we introduce Leap2Trend, a novel approach to instant detection of research trends. Leap2Trend relies on temporal word embeddings (word2vec) to track the dynamics of similarities between pairs of keywords, their rankings and respective uprankings (ascents) over time. We applied Leap2Trend to two scientific corpora on different research areas, namely computer science and bioinformatics and we evaluated it against two gold standards Google Trends hits and Google Scholar citations. The obtained results reveal the effectiveness of our approach to detect trends with more than 80% accuracy and 90% precision in some cases. Such significant findings evidence the utility of our Leap2Trend approach for tracking and detecting emerging research trends instantly.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2957440
IEEE ACCESS
Keywords
DocType
Volume
Citation counts,Google scholar,Google trends,temporal word embedding,trend analysis
Journal
7
ISSN
Citations 
PageRank 
2169-3536
1
0.35
References 
Authors
0
4
Name
Order
Citations
PageRank
Amna Dridi111.36
Mohamed Medhat Gaber2108171.17
R. Muhammad Atif Azad310.35
Jagdev Bhogal4124.08