research-article

Topic Modelling for Identification of Vaccine Reactions in Twitter

Authors:

Sedigheh Khademi Habibabadi,

Pari Delir HaghighiAuthors Info & Claims

ACSW '19: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 31, Pages 1 - 10

https://doi.org/10.1145/3290688.3290735

Published: 29 January 2019 Publication History

Abstract

Background: Detection of vaccine safety signals depends on various established reporting systems, where there is inevitably a lag between an adverse reaction to a vaccine and the reporting of it, and subsequent processing of reports. Therefore, it is desirable to try and detect safety signals earlier, ideally close to real-time. Extensive use of social media has provided a platform for sharing and seeking health-related information, and the immediacy of social media conversations mean that they are an ideal candidate for early detection of vaccine safety signals. The objective of this study is to evaluate topic models for identifying user posts on Twitter that most likely contain vaccine safety signals. This is an initial step in the overall research to determine if reliable vaccine safety signals can be detected in social media streams. The techniques used were focused on identifying the model design and number of topics that best revealed documents that contained vaccine safety signals, to assist with dimension reduction and subsequent labelling of the text data. The study compared Gensim LDA, MALLET, and jLDADMM DMM models to determine the most effective model for detecting vaccine safety signals, assisted by an evaluation process that used an adjusted F-Scoring technique over a labelled subset of the documents.

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Cited By

Faizah Lin B(2023)Visualizing Change and Correlation of Topics With LDA and Agglomerative Clustering on COVID-19 Vaccine TweetsIEEE Access10.1109/ACCESS.2023.327897911(51647-51656)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3278979
Masyhuri M(2022)Topic Modelling Application for Determining Competitiveness Factors of the Small Business FirmsInternational Journal of Social Science and Business10.23887/ijssb.v6i2.431646:2(174-182)Online publication date: 28-Jun-2022
https://doi.org/10.23887/ijssb.v6i2.43164
Khademi Habibabadi SDelir Haghighi PBurstein FButtery J(2022)Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification StudyJMIR Medical Informatics10.2196/3430510:6(e34305)Online publication date: 16-Jun-2022
https://doi.org/10.2196/34305
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Topic Modelling for Identification of Vaccine Reactions in Twitter

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cover image ACM Other conferences

ACSW '19: Proceedings of the Australasian Computer Science Week Multiconference

January 2019

486 pages

ISBN:9781450366038

DOI:10.1145/3290688

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Macquarie University-Sydney

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Published: 29 January 2019

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ACSW 2019

ACSW 2019: Australasian Computer Science Week 2019

January 29 - 31, 2019

NSW, Sydney, Australia

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ACSW '19 Paper Acceptance Rate 61 of 141 submissions, 43%;

Overall Acceptance Rate 61 of 141 submissions, 43%

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Cited By

Faizah Lin B(2023)Visualizing Change and Correlation of Topics With LDA and Agglomerative Clustering on COVID-19 Vaccine TweetsIEEE Access10.1109/ACCESS.2023.327897911(51647-51656)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3278979
Masyhuri M(2022)Topic Modelling Application for Determining Competitiveness Factors of the Small Business FirmsInternational Journal of Social Science and Business10.23887/ijssb.v6i2.431646:2(174-182)Online publication date: 28-Jun-2022
https://doi.org/10.23887/ijssb.v6i2.43164
Khademi Habibabadi SDelir Haghighi PBurstein FButtery J(2022)Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification StudyJMIR Medical Informatics10.2196/3430510:6(e34305)Online publication date: 16-Jun-2022
https://doi.org/10.2196/34305
Lau SLee MTeoh M(2022)Understanding Public Sentiment Towards a Public Rally Using Text and Social Media Analytic2022 IEEE International Conference on Computing (ICOCO)10.1109/ICOCO56118.2022.10031692(16-20)Online publication date: 14-Nov-2022
https://doi.org/10.1109/ICOCO56118.2022.10031692
Gupta IChatterjee IGupta N(2022)Latent Semantic Analysis based Real-world Application of Topic Modeling: A Review Study2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS)10.1109/ICAIS53314.2022.9742848(1142-1149)Online publication date: 23-Feb-2022
https://doi.org/10.1109/ICAIS53314.2022.9742848
Buttery JClothier H(2022)Information systems for vaccine safety surveillanceHuman Vaccines & Immunotherapeutics10.1080/21645515.2022.210017318:6Online publication date: 26-Sep-2022
https://doi.org/10.1080/21645515.2022.2100173
Feng YChen H(2022)Evolving Consumer Responses to Social Issue Campaigns: A Data-Mining Case of COVID-19 Ads on YouTubeJournal of Interactive Advertising10.1080/15252019.2022.206377022:2(195-206)Online publication date: 15-Jun-2022
https://doi.org/10.1080/15252019.2022.2063770
Luo CJi KTang YDu Z(2021)Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining ApproachJournal of Medical Internet Research10.2196/3071523:8(e30715)Online publication date: 27-Aug-2021
https://doi.org/10.2196/30715
Vijaykumar SRaamkumar AMcCarty KMutumbwa CMustafa JAu C(2021)Themes, communities and influencers of online probiotics chatter: A retrospective analysis from 2009-2017PLOS ONE10.1371/journal.pone.025809816:10(e0258098)Online publication date: 21-Oct-2021
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Luo CChen ACui BLiao W(2021)Exploring public perceptions of the COVID-19 vaccine online from a cultural perspectiveTelematics and Informatics10.1016/j.tele.2021.10171265:COnline publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.tele.2021.101712
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