Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore

Ridhwan, Khairiyah Mohamed; Hargreaves, Carol Anne

doi:10.1016/j.jjimei.2021.100021

Cited by 99 publications

(57 citation statements)

References 14 publications

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“…Good and panic were the dominant sentiments of the public during the pandemic. It showed a high sentiment intensity trend in the early stage, peaking after experiencing dramatic fluctuations and slowly decreasing later [ 65 ]. The result was influenced by the developmental stage of the pandemic and the occurrence of popular events.…”

Section: Resultsmentioning

confidence: 99%

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Yang

Zhang

et al. 2022

International Journal of Disaster Risk Reduction

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Novel coronavirus pneumonia has had a significant impact on people's lives and psychological health. We developed a stage model to analyse the spatial and temporal distribution of public panic during the two waves of the coronavirus disease 2019 (COVID-19) pandemic. We used tweets with geographic location data from the popular hashtag ‘Lockdown Diary’ recorded from 23 January to April 8, 2020, and ‘Nanjing Outbreak’ recorded from 21 July to 1 September 2021 on Weibo. Combining the lexicon-based sentiment analysis and the grounded theory approach, this panic model could explain people's panic and behavioural responses in different areas at different stages of the pandemic. Next, we used the latent Dirichlet allocation topic model to reconfirm the panic model. The results showed that public sentiments fluctuated strongly in the early stages; in this case, panic and prayers were the dominant sentiments. In terms of spatial distribution, public panic showed hierarchical and neighbourhood diffusion, with highly assertive expressions of sentiment at the outbreak sites, economically developed areas, and areas surrounding the outbreak. Most importantly, we considered that public panic was affected by the 17 specific topics extracted based on the perceived and actual distance of the pandemic, thus stimulating the process of panic from minimal, acute, and mild panic to perceived rationality. Consequently, the public's behavioural responses shifted from delayed, negative, and positive, to rational behavioural responses. This study presents a novel approach to explore public panic from both a time and space perspective and provides some suggestions in response to future pandemics.

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Section: Resultsmentioning

confidence: 99%

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Yang

Zhang

et al. 2022

International Journal of Disaster Risk Reduction

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“…Our search terms included a combination of “vaccine” and COVID-19–related terms (“covid,” “coronavirus,” “covid19,” “covid-19,” “ncov2019,” and “SARS-CoV-2”) to retrieve tweets published between January 1, 2021, and April 30, 2021. Snscrape and Getoldtweets are popular Python libraries that have been used in several infoveillance studies to capture Twitter data [ 26 , 34 , 35 ]. We ensured removal of retweets and duplicates so that the data set contained only the original tweets made by the users.…”

Section: Methodsmentioning

confidence: 99%

Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts

Ranganathan¹,

Desai²,

Shah³

et al. 2022

JMIR Infodemiology

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Background A global rollout of vaccinations is currently underway to mitigate and protect people from the COVID-19 pandemic. Several individuals have been using social media platforms such as Twitter as an outlet to express their feelings, concerns, and opinions about COVID-19 vaccines and vaccination programs. This study examined COVID-19 vaccine–related tweets from January 1, 2020, to April 30, 2021, to uncover the topics, themes, and variations in sentiments of public Twitter users. Objective The aim of this study was to examine key themes and topics from COVID-19 vaccine–related English tweets posted by individuals, and to explore the trends and variations in public opinions and sentiments. Methods We gathered and assessed a corpus of 2.94 million COVID-19 vaccine–related tweets made by 1.2 million individuals. We used CoreX topic modeling to explore the themes and topics underlying the tweets, and used VADER sentiment analysis to compute sentiment scores and examine weekly trends. We also performed qualitative content analysis of the top three topics pertaining to COVID-19 vaccination. Results Topic modeling yielded 16 topics that were grouped into 6 broader themes underlying the COVID-19 vaccination tweets. The most tweeted topic about COVID-19 vaccination was related to vaccination policy, specifically whether vaccines needed to be mandated or optional (13.94%), followed by vaccine hesitancy (12.63%) and postvaccination symptoms and effects (10.44%) Average compound sentiment scores were negative throughout the 16 weeks for the topics postvaccination symptoms and side effects and hoax/conspiracy. However, consistent positive sentiment scores were observed for the topics vaccination disclosure, vaccine efficacy, clinical trials and approvals, affordability, regulation, distribution and shortage, travel, appointment and scheduling, vaccination sites, advocacy, opinion leaders and endorsement, and gratitude toward health care workers. Reversal in sentiment scores in a few weeks was observed for the topics vaccination eligibility and hesitancy. Conclusions Identification of dominant themes, topics, sentiments, and changing trends about COVID-19 vaccination can aid governments and health care agencies to frame appropriate vaccination programs, policies, and rollouts.

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“…The extreme negative and positive scenarios are indicated by −1 and 1. Following the common trend in previous research, we mapped compound scores ≥ 0.05 to positive, ≤−0.05 to negative and the remaining to neutral sentiments during our evaluations [13,14]. Similarly, TextBlob also returns a polarity score within [−1, 1], but negative, zero and positive values are recognised as negative, neutral and positive sentiments, commonly [14,16].…”

Section: Resultsmentioning

confidence: 99%

“…Unsupervised Lexicon-based Approaches: Considering the unavailability of prelabelled data by the time of data labelling, there was a tendency to use unsupervised lexicon-based approaches for sentiment labelling. VADER [12][13][14][15] and TextBlob [8,14,16] were found to be the popularly used such unsupervised tools. VADER (Valence Aware Dictionary for sEntiment Reasoning) is a simple lexicon-and rule-based model designed for general sentiment analysis [17].…”

mentioning

confidence: 99%

TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments

Hettiarachchi

Al-Turkey

Adedoyin-Olowe

et al. 2022

Data

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Even though social media contain rich information on events and public opinions, it is impractical to manually filter this information due to data’s vast generation and dynamicity. Thus, automated extraction mechanisms are invaluable to the community. We need real data with ground truth labels to build/evaluate such systems. Still, to the best of our knowledge, no available social media dataset covers continuous periods with event and sentiment labels together except for events or sentiments. Datasets without time gaps are huge due to high data generation and require extensive effort for manual labelling. Different approaches, ranging from unsupervised to supervised, have been proposed by previous research targeting such datasets. However, their generic nature mainly fails to capture event-specific sentiment expressions, making them inappropriate for labelling event sentiments. Filling this gap, we propose a novel data annotation approach in this paper involving several neural networks. Our approach outperforms the commonly used sentiment annotation models such as VADER and TextBlob. Also, it generates probability values for all sentiment categories besides providing a single category per tweet, supporting aggregated sentiment analyses. Using this approach, we annotate and release a dataset named TED-S, covering two diverse domains, sports and politics. TED-S has complete subsets of Twitter data streams with both sub-event and sentiment labels, providing the ability to support event sentiment-based research.

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Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore

Cited by 99 publications

References 14 publications

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Spatial evolution patterns of public panic on Chinese social networks amidst the COVID-19 pandemic

Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts

TED-S: Twitter Event Data in Sports and Politics with Aggregated Sentiments

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