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Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage

Jay Paul Morgan Orcid Logo, Frederic Boy Orcid Logo

2024 International Conference on Machine Learning and Applications (ICMLA), Pages: 876 - 881

Swansea University Authors: Jay Paul Morgan Orcid Logo, Frederic Boy Orcid Logo

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DOI (Published version): 10.1109/icmla61862.2024.00127

Abstract

Web users worldwide rely on search engines daily, querying diverse terms to locate pertinent information. Due to the omnipresence of search engine in contemporary lives, we hypothesise that finely grained analyses of these search terms volume can offer valuable insights into societal trends, potenti...

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Published in: 2024 International Conference on Machine Learning and Applications (ICMLA)
ISBN: 979-8-3503-7489-6 979-8-3503-7488-9
ISSN: 1946-0740 1946-0759
Published: IEEE 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa67700
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last_indexed 2025-03-15T05:30:42Z
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spelling 2025-03-14T12:49:49.5474001 v2 67700 2024-09-17 Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage df9a27bcf77b4769c2ebbb702b587491 0000-0003-3719-362X Jay Paul Morgan Jay Paul Morgan true false 43e704698d5dbbac3734b7cd0fef60aa 0000-0003-1373-6634 Frederic Boy Frederic Boy true false 2024-09-17 MACS Web users worldwide rely on search engines daily, querying diverse terms to locate pertinent information. Due to the omnipresence of search engine in contemporary lives, we hypothesise that finely grained analyses of these search terms volume can offer valuable insights into societal trends, potentially reflecting economic conditions and overall quality of life. We examined Google Trends data, sampled hourly, for 61 specific search terms, revealing three primary patterns in how these keywords are used across daily search activities. We employ Dynamic Time Warping to compare the search volumes of these keywords, then apply hierarchical clustering for categorisation. Additionally, a Recurrent Neural Network (RNN) is used to learn the 24-hour time series patterns of these searches. We evaluate the RNN's effectiveness through two experiments, assessing its capacity to generalise across diverse keyword types and various dates. Incorporated into a broader framework, this RNN could potentially help monitor social welfare in near real time and guide policymaking addressing fundamental societal challenges. Conference Paper/Proceeding/Abstract 2024 International Conference on Machine Learning and Applications (ICMLA) 876 881 IEEE 979-8-3503-7489-6 979-8-3503-7488-9 1946-0740 1946-0759 Recurrent neural networks; Heuristic algorithms; Time series analysis; Keyword search; Machine learning; Search engines; Market research; Real-time systems; Internet; Monitoring 18 12 2024 2024-12-18 10.1109/icmla61862.2024.00127 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required Swansea University 2025-03-14T12:49:49.5474001 2024-09-17T10:21:51.4795863 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Jay Paul Morgan 0000-0003-3719-362X 1 Frederic Boy 0000-0003-1373-6634 2 67700__33454__af08b2bb55214bd7aac685431fe58700.pdf Google_Trends__Submission_-2.pdf 2025-01-31T07:00:48.1795431 Output 733346 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/ 300
title Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage
spellingShingle Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage
Jay Paul Morgan
Frederic Boy
title_short Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage
title_full Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage
title_fullStr Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage
title_full_unstemmed Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage
title_sort Predicting Temporal Patterns in Keyword Searches with Recurrent Neural Networks — Phenotyping Human Behaviour from Search Engine Usage
author_id_str_mv df9a27bcf77b4769c2ebbb702b587491
43e704698d5dbbac3734b7cd0fef60aa
author_id_fullname_str_mv df9a27bcf77b4769c2ebbb702b587491_***_Jay Paul Morgan
43e704698d5dbbac3734b7cd0fef60aa_***_Frederic Boy
author Jay Paul Morgan
Frederic Boy
author2 Jay Paul Morgan
Frederic Boy
format Conference Paper/Proceeding/Abstract
container_title 2024 International Conference on Machine Learning and Applications (ICMLA)
container_start_page 876
publishDate 2024
institution Swansea University
isbn 979-8-3503-7489-6
979-8-3503-7488-9
issn 1946-0740
1946-0759
doi_str_mv 10.1109/icmla61862.2024.00127
publisher IEEE
college_str Faculty of Science and Engineering
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hierarchy_parent_id facultyofscienceandengineering
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department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Web users worldwide rely on search engines daily, querying diverse terms to locate pertinent information. Due to the omnipresence of search engine in contemporary lives, we hypothesise that finely grained analyses of these search terms volume can offer valuable insights into societal trends, potentially reflecting economic conditions and overall quality of life. We examined Google Trends data, sampled hourly, for 61 specific search terms, revealing three primary patterns in how these keywords are used across daily search activities. We employ Dynamic Time Warping to compare the search volumes of these keywords, then apply hierarchical clustering for categorisation. Additionally, a Recurrent Neural Network (RNN) is used to learn the 24-hour time series patterns of these searches. We evaluate the RNN's effectiveness through two experiments, assessing its capacity to generalise across diverse keyword types and various dates. Incorporated into a broader framework, this RNN could potentially help monitor social welfare in near real time and guide policymaking addressing fundamental societal challenges.
published_date 2024-12-18T05:23:30Z
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score 11.089386

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