Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets

Hamzah Osop; Jie Yang Wong; Shwe Waddy Lwin; Chei Sian Lee

doi:10.1007/978-981-99-8085-7_15

Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets

Hamzah Osop^*, Jie Yang Wong, Shwe Waddy Lwin, Chei Sian Lee

^*Corresponding author for this work

Nanyang Technological University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Twitter has become a platform for online health information seeking and sharing, especially during the COVID-19 pandemic. Prior studies revealed evidence of health information topics in Twitter discussions, but the analysis was primarily on tweets made before or during the pandemic. This research investigated tweets made pre- and current-COVID era for health information content and information encountering. COVD19- and COVD23 + datasets containing 13,667 and 9,855 tweets were analysed and clustered into four health-related topics using Convolutional Neural Network. We compared the tweet distribution and tweet emotions for content and sentiment analysis. We found a higher proportion of tweets in the Medication & Treatment topic and greater Negative and Ambiguous emotions in the COVD23 + dataset, with tweet discussions focusing more on diabetes and its relationship with COVID-19. Elements of information encountering exist based on Panahi’s and Jiang’s IE model, with a decrease in encountering diabetes prevention and management information during the pandemic.

Original language	English
Title of host publication	Leveraging Generative Intelligence in Digital Libraries
Subtitle of host publication	Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings
Editors	Dion H. Goh, Shu-Jiun Chen, Suppawong Tuarob
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	167-177
Number of pages	11
ISBN (Print)	9789819980840
DOIs	https://doi.org/10.1007/978-981-99-8085-7_15
Publication status	Published - 2023
Externally published	Yes
Event	25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023 - Taipei, Taiwan, Province of China Duration: Dec 4 2023 → Dec 7 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14457 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023
Country/Territory	Taiwan, Province of China
City	Taipei
Period	12/4/23 → 12/7/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

COVID-19
Diabetes
Emotion Analysis
Information Encountering
Sentiment Analysis
Topic Modelling

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-99-8085-7_15

Cite this

Osop, H., Wong, J. Y., Lwin, S. W., & Lee, C. S. (2023). Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets. In D. H. Goh, S.-J. Chen, & S. Tuarob (Eds.), Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings (pp. 167-177). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14457 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-99-8085-7_15

Osop, Hamzah ; Wong, Jie Yang ; Lwin, Shwe Waddy et al. / Health Information Encountering : Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets. Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. editor / Dion H. Goh ; Shu-Jiun Chen ; Suppawong Tuarob. Springer Science and Business Media Deutschland GmbH, 2023. pp. 167-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3fa48addcbf74a0d92b1e3c28cb5e37f,

title = "Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets",

abstract = "Twitter has become a platform for online health information seeking and sharing, especially during the COVID-19 pandemic. Prior studies revealed evidence of health information topics in Twitter discussions, but the analysis was primarily on tweets made before or during the pandemic. This research investigated tweets made pre- and current-COVID era for health information content and information encountering. COVD19- and COVD23 + datasets containing 13,667 and 9,855 tweets were analysed and clustered into four health-related topics using Convolutional Neural Network. We compared the tweet distribution and tweet emotions for content and sentiment analysis. We found a higher proportion of tweets in the Medication & Treatment topic and greater Negative and Ambiguous emotions in the COVD23 + dataset, with tweet discussions focusing more on diabetes and its relationship with COVID-19. Elements of information encountering exist based on Panahi{\textquoteright}s and Jiang{\textquoteright}s IE model, with a decrease in encountering diabetes prevention and management information during the pandemic.",

keywords = "COVID-19, Diabetes, Emotion Analysis, Information Encountering, Sentiment Analysis, Topic Modelling",

author = "Hamzah Osop and Wong, {Jie Yang} and Lwin, {Shwe Waddy} and Lee, {Chei Sian}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023 ; Conference date: 04-12-2023 Through 07-12-2023",

year = "2023",

doi = "10.1007/978-981-99-8085-7_15",

language = "English",

isbn = "9789819980840",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "167--177",

editor = "Goh, {Dion H.} and Shu-Jiun Chen and Suppawong Tuarob",

booktitle = "Leveraging Generative Intelligence in Digital Libraries",

address = "Germany",

}

Osop, H, Wong, JY, Lwin, SW & Lee, CS 2023, Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets. in DH Goh, S-J Chen & S Tuarob (eds), Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14457 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 167-177, 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Taipei, Taiwan, Province of China, 12/4/23. https://doi.org/10.1007/978-981-99-8085-7_15

Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets. / Osop, Hamzah; Wong, Jie Yang; Lwin, Shwe Waddy et al.
Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. ed. / Dion H. Goh; Shu-Jiun Chen; Suppawong Tuarob. Springer Science and Business Media Deutschland GmbH, 2023. p. 167-177 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14457 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Health Information Encountering

T2 - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023

AU - Osop, Hamzah

AU - Wong, Jie Yang

AU - Lwin, Shwe Waddy

AU - Lee, Chei Sian

PY - 2023

Y1 - 2023

N2 - Twitter has become a platform for online health information seeking and sharing, especially during the COVID-19 pandemic. Prior studies revealed evidence of health information topics in Twitter discussions, but the analysis was primarily on tweets made before or during the pandemic. This research investigated tweets made pre- and current-COVID era for health information content and information encountering. COVD19- and COVD23 + datasets containing 13,667 and 9,855 tweets were analysed and clustered into four health-related topics using Convolutional Neural Network. We compared the tweet distribution and tweet emotions for content and sentiment analysis. We found a higher proportion of tweets in the Medication & Treatment topic and greater Negative and Ambiguous emotions in the COVD23 + dataset, with tweet discussions focusing more on diabetes and its relationship with COVID-19. Elements of information encountering exist based on Panahi’s and Jiang’s IE model, with a decrease in encountering diabetes prevention and management information during the pandemic.

AB - Twitter has become a platform for online health information seeking and sharing, especially during the COVID-19 pandemic. Prior studies revealed evidence of health information topics in Twitter discussions, but the analysis was primarily on tweets made before or during the pandemic. This research investigated tweets made pre- and current-COVID era for health information content and information encountering. COVD19- and COVD23 + datasets containing 13,667 and 9,855 tweets were analysed and clustered into four health-related topics using Convolutional Neural Network. We compared the tweet distribution and tweet emotions for content and sentiment analysis. We found a higher proportion of tweets in the Medication & Treatment topic and greater Negative and Ambiguous emotions in the COVD23 + dataset, with tweet discussions focusing more on diabetes and its relationship with COVID-19. Elements of information encountering exist based on Panahi’s and Jiang’s IE model, with a decrease in encountering diabetes prevention and management information during the pandemic.

KW - COVID-19

KW - Diabetes

KW - Emotion Analysis

KW - Information Encountering

KW - Sentiment Analysis

KW - Topic Modelling

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M3 - Conference contribution

AN - SCOPUS:85180158334

SN - 9789819980840

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 167

EP - 177

BT - Leveraging Generative Intelligence in Digital Libraries

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PB - Springer Science and Business Media Deutschland GmbH

Y2 - 4 December 2023 through 7 December 2023

ER -

Osop H, Wong JY, Lwin SW, Lee CS. Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets. In Goh DH, Chen SJ, Tuarob S, editors, Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration - 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 167-177. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-99-8085-7_15

Health Information Encountering: Topic Modelling and Sentiment Analysis of Pre- and Current-COVID-19 Tweets

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Keywords

UN SDGs

Access to Document

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Cite this