More Web Proxy on the site http://driver.im/

research-article

Dynamic Topic Modeling of Covid-19 Vaccine-Related Tweets

Authors:

Anubhav Sharma,

Arjun ChoudhryAuthors Info & Claims

DSDE '22: Proceedings of the 2022 5th International Conference on Data Storage and Data Engineering

Pages 79 - 84

https://doi.org/10.1145/3528114.3528127

Published: 24 June 2022 Publication History

Abstract

The Covid-19 pandemic has made a huge impact on the world. Vaccines are regarded as the universal solution to mitigate the spread of the pandemic. Vaccination programs have been initiated by all countries in the past one year or so. The public opinion about vaccinations has been dynamically changing during this period. We intend to track the perception of the masses since the arrival of the vaccines, through social media posts, and reflect on the reasons behind the dynamically evolving ideas of people. For this purpose, we propose the use of Latent Dirichlet Allocation (LDA) for topic modeling from vaccine-related discussions on the popular social media platform Twitter, in five temporal phases, in the duration of 20 December 2020 to 16 October 2021. The time windows are determined such that the tweets are equally distributed in each time slice. The ten most relevant terms in the top-10 topics in each time window are determined and presented in the form of bar charts. The relevancy of a term is interpreted as the sum of probabilistic scores associated with that term in the top-10 topics identified by LDA in a particular time period. The bar charts are further analyzed for inferring the topics of discussion in a particular phase of time.

References

[1]

He, F., Deng, Y. and Li, W., 2020. Coronavirus disease 2019: What we know?. Journal of medical virology, 92(7), pp.719-725.

[2]

Ferrara, E., 2020. What types of COVID-19 conspiracies are populated by Twitter bots?. arXiv preprint arXiv:2004.09531.

[3]

Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A. and Sheikh, A., 2021. Artificial intelligence–enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study. Journal of medical Internet research, 23(4), p.e26627.

[4]

Melton, C.A., Olusanya, O.A., Ammar, N. and Shaban-Nejad, A., 2021. Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence. Journal of Infection and Public Health, 14(10), pp.1505-1512.

[5]

Sha, H., Hasan, M.A., Mohler, G. and Brantingham, P.J., 2020. Dynamic topic modeling of the COVID-19 Twitter narrative among US governors and cabinet executives. arXiv preprint arXiv:2004.11692.

[6]

Kwok, S.W.H., Vadde, S.K. and Wang, G., 2021. Tweet topics and sentiments relating to COVID-19 vaccination among Australian Twitter users: Machine learning analysis. Journal of medical Internet research, 23(5), p.e26953.

[7]

Ghasiya, P. and Okamura, K., 2021. Investigating COVID-19 News Across Four Nations: A Topic Modeling and Sentiment Analysis Approach. IEEE Access, 9, pp.36645-36656.

[8]

Zamani, M., Schwartz, H.A., Eichstaedt, J., Guntuku, S.C., Ganesan, A.V., Clouston, S. and Giorgi, S., 2020, November. Understanding weekly COVID-19 concerns through dynamic content-specific LDA topic modeling. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing (Vol. 2020, p. 193). NIH Public Access.

[9]

Shahriar, Khandaker Tayef, Iqbal H. Sarker, Muhammad Nazrul Islam, and Mohammad Ali Moni. "A Dynamic Topic Identification and Labeling Approach of COVID-19 Tweets." arXiv preprint arXiv:2109.02462 (2021).

[10]

Blei, D.M., Ng, A.Y. and Jordan, M.I., 2003. Latent dirichlet allocation. the Journal of machine Learning research, 3, pp.993-1022.

[11]

Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y. and Zhao, L., 2019. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), pp.15169-15211.

Digital Library

[12]

Porter, K., 2018. Analyzing the DarkNetMarkets subreddit for evolutions of tools and trends using LDA topic modeling. Digital Investigation, 26, pp.S87-S97.

Digital Library

[13]

Yin, H., Song, X., Yang, S. and Li, J., 2021. Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions. arXiv preprint arXiv:2111.04415.

[14]

https://www.kaggle.com/gpreda/all-covid19-vaccines-tweets [Last accessed on 30th Dec 2021]

[15]

Susan, S. and Keshari, J., 2019. Finding significant keywords for document databases by two-phase Maximum Entropy Partitioning. Pattern Recognition Letters, 125, pp.195-205.

[16]

Vashishtha, S. and Susan, S., 2018, December. Fuzzy logic based dynamic plotting of mood swings from tweets. In International Conference on Innovations in Bio-Inspired Computing and Applications (pp. 129-139). Springer, Cham.

[17]

Ghosh, Mainak, Kirtika Gupta, and Seba Susan. "Aspect-Based Unsupervised Negative Sentiment Analysis." In Intelligent Data Communication Technologies and Internet of Things, pp. 335-344. Springer, Singapore, 2021.

[18]

Vashishtha, S. and Susan, S., 2020, July. Fuzzy interpretation of word polarity scores for unsupervised sentiment analysis. In 2020 11th international conference on computing, communication and networking technologies (ICCCNT) (pp. 1-6). IEEE.

[19]

Älgå, A., Eriksson, O. and Nordberg, M., 2020. Analysis of scientific publications during the early phase of the COVID-19 pandemic: topic modeling study. Journal of medical Internet research, 22(11), p.e21559.

[20]

Hu, T., Wang, S., Luo, W., Yan, Y., Zhang, M., Huang, X., Liu, R., Ly, K., Kacker, V. and Li, Z., 2021. Revealing public opinion towards COVID-19 vaccines using Twitter data in the United States: a spatiotemporal perspective. medRxiv.

[21]

Niu, Q., Liu, J., Nagai-Tanima, M., Aoyama, T., Masaya, K., Shinohara, Y. and Matsumura, N., 2021. Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis. medRxiv.

Cited By

Sharma MChoudhary GSusan S(2023)Resume Classification using Elite Bag-of-Words Approach2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)10.1109/ICSSIT55814.2023.10061036(1409-1413)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICSSIT55814.2023.10061036

Recommendations

Detecting and analyzing topics of massive COVID-19 related tweets for various countries
Highlights
- The topics of tweets related to COVID-19 are modeled for the entire world and the 32 most affected countries.
- Tweets are analyzed for location tagging.
- Countries' Sentiments are categorized into negative countries and positive ...
Abstract
With the flare-up of the COVID-19 infection since 2020, COVID-19 has been one of the hottest topics on Twitter. Topic modeling is one of the most popular analyses, which extracts the topics from the text. This paper proposes a method to extract ...
Graphical abstract

Display Omitted
Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by Leveraging Hashtags and Sentiment Lexicon
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are ...
Topic and sentiment aware microblog summarization for twitter
Abstract
Recent advances in microblog content summarization has primarily viewed this task in the context of traditional multi-document summarization techniques where a microblog post or their collection form one document. While these techniques already ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

DSDE '22: Proceedings of the 2022 5th International Conference on Data Storage and Data Engineering

February 2022

124 pages

ISBN:9781450395724

DOI:10.1145/3528114

Copyright © 2022 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 June 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

DSDE 2022

DSDE 2022: 2022 the 5th International Conference on Data Storage and Data Engineering

February 25 - 27, 2022

Sanya, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
73
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)1

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Sharma MChoudhary GSusan S(2023)Resume Classification using Elite Bag-of-Words Approach2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)10.1109/ICSSIT55814.2023.10061036(1409-1413)Online publication date: 23-Jan-2023
https://doi.org/10.1109/ICSSIT55814.2023.10061036

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents