Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends

Dridi, Amna and Gaber, Mohamed Medhat and Azad, R. Muhammad Atif and Bhogal, Jagdev (2019) Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends. IEEE Access, 7. p. 1. ISSN 2169-3536

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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.

Item Type: Article
Identification Number: https://doi.org/10.1109/ACCESS.2019.2957440
29 November 2019Accepted
Uncontrolled Keywords: citation counts, Google Scholar, Google trends, temporal word embedding, trend analysis
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Mohamed Gaber
Date Deposited: 05 Dec 2019 17:20
Last Modified: 12 Jan 2022 12:58
URI: https://www.open-access.bcu.ac.uk/id/eprint/8553

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