ArticleSN computer science2021
Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from a State in Australia.
Article in SN computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 54 citations in OpenAlex.
- Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.Behavioral sciences (Basel, Switzerland) · 2024Article
- Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic.Health information science and systems · 2023Article
- Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts.Journal of medical Internet research · 2023Article
- Deep Learning Model for COVID-19 Sentiment Analysis on Twitter.New generation computing · 2023Article
- Enhanced sentiment analysis regarding COVID-19 news from global channels.Journal of computational social science · 2023Article
- Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines.PloS one · 2023Article
- Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context.Array (New York, N.Y.) · 2022Article
- Papers Please - Predictive Factors of National and International Attitudes Toward Immunity and Vaccination Passports: Online Representative Surveys.JMIR public health and surveillance · 2022Article
- COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.Informatics in medicine unlocked · 2022Article
- Quantifying societal emotional resilience to natural disasters from geo-located social media content.PloS one · 2022Article
- Sentiment analysis and topic modeling for COVID-19 vaccine discussions.World wide web · 2022Article
- Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia.IEEE transactions on computational social systems · 2021Article
- COVIDSenti: A Large-Scale Benchmark Twitter Data Set for COVID-19 Sentiment Analysis.IEEE transactions on computational social systems · 2021Article
- COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication.Journal of medical Internet research · 2021Article
- The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement.IEEE access : practical innovations, open solutions · 2021Article
- Article
- Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets.JMIR infodemiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has caused unprecedented impacts to people's daily life around the world. Various measures and policies such as lockdown and social-distancing are implemented by governments to combat the disease during the pandemic period. These measures and policies as well as virus itself may cause different mental health issues to people such as depression, anxiety, sadness, etc. In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period. Different from the existing work that mostly focuses on the country-level and static sentiment analysis, we analyse the sentiment dynamics at the fine-grained local government areas (LGAs). Based on the analysis of around 94 million tweets that posted by around 183 thousand users located at different LGAs in NSW in 5 months, we found that people in NSW showed an overall positive sentimental polarity and the COVID-19 pandemic decreased the overall positive sentimental polarity during the pandemic period. The fine-grained analysis of sentiment in LGAs found that despite the dominant positive sentiment most of days during the study period, some LGAs experienced significant sentiment changes from positive to negative. This study also analysed the sentimental dynamics delivered by the hot topics in Twitter such as government policies (e.g. the Australia's JobKeeper program, lockdown, social-distancing) as well as the focused social events (e.g. the Ruby Princess Cruise). The results showed that the policies and events did affect people's overall sentiment, and they affected people's overall sentiment differently at different stages.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.