Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends
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 literatur...
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doaj-e2f796857c404e94924a000fd5a436dd2021-03-30T00:28:54ZengIEEEIEEE Access2169-35362019-01-01717641417642810.1109/ACCESS.2019.29574408920056Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific TrendsAmna Dridi0https://orcid.org/0000-0002-0185-103XMohamed Medhat Gaber1https://orcid.org/0000-0003-0339-4474R. Muhammad Atif Azad2https://orcid.org/0000-0002-4013-5415Jagdev Bhogal3https://orcid.org/0000-0002-1160-9140School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.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.https://ieeexplore.ieee.org/document/8920056/Citation countsGoogle scholarGoogle trendstemporal word embeddingtrend analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Amna Dridi Mohamed Medhat Gaber R. Muhammad Atif Azad Jagdev Bhogal |
spellingShingle |
Amna Dridi Mohamed Medhat Gaber R. Muhammad Atif Azad Jagdev Bhogal Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends IEEE Access Citation counts Google scholar Google trends temporal word embedding trend analysis |
author_facet |
Amna Dridi Mohamed Medhat Gaber R. Muhammad Atif Azad Jagdev Bhogal |
author_sort |
Amna Dridi |
title |
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends |
title_short |
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends |
title_full |
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends |
title_fullStr |
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends |
title_full_unstemmed |
Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends |
title_sort |
leap2trend: a temporal word embedding approach for instant detection of emerging scientific trends |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
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. |
topic |
Citation counts Google scholar Google trends temporal word embedding trend analysis |
url |
https://ieeexplore.ieee.org/document/8920056/ |
work_keys_str_mv |
AT amnadridi leap2trendatemporalwordembeddingapproachforinstantdetectionofemergingscientifictrends AT mohamedmedhatgaber leap2trendatemporalwordembeddingapproachforinstantdetectionofemergingscientifictrends AT rmuhammadatifazad leap2trendatemporalwordembeddingapproachforinstantdetectionofemergingscientifictrends AT jagdevbhogal leap2trendatemporalwordembeddingapproachforinstantdetectionofemergingscientifictrends |
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