Covidsenti
A Large-Scale Benchmark Twitter Data Set for Covid-19 Sentiment Analysis
Bibliographic Data
| ID | 19601585 |
|---|---|
| Authors | Usman Naseem (0000-0003-0191-7171, University of Technology Sydney), Imran Razzak (0000-0002-3930-6600, Deakin University), Matloob Khushi (0000-0001-7792-2327, University of Technology Sydney), Peter Eklund (0000-0003-2313-8603, Deakin University), Jinman Kim (0000-0001-5960-1060, University of Technology Sydney) |
| Year | 2021 |
| Volume | 8 |
| Issue | 4 |
| Pages | 1003-1015 |
| Publication date | 2021-01-29 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (PUBLISHER • US) |
| DOI | 10.1109/tcss.2021.3051189 |
| PMID | 35783149 |
| OpenAlex | W3127056209 |
| Language | EN |
| Citations received | 36 |
| References cited | 32 |
Social media (and the world at large) have been awash with news of the COVID-19 pandemic. With the passage of time, news and awareness about COVID-19 spread like the pandemic itself, with an explosion of messages, updates, videos, and posts. Mass hysteria manifest as another concern in addition to the health risk that COVID-19 presented. Predictably, public panic soon followed, mostly due to misconceptions, a lack of information, or sometimes outright misinformation about COVID-19 and its impacts. It is thus timely and important to conduct an ex post facto assessment of the early information flows during the pandemic on social media, as well as a case study of evolving public opinion on social media which is of general interest. This study aims to inform policy that can be applied to social media platforms; for example, determining what degree of moderation is necessary to curtail misinformation on social media. This study also analyzes views concerning COVID-19 by focusing on people who interact and share social media on Twitter. As a platform for our experiments, we present a new large-scale sentiment data set COVIDSENTI, which consists of 90 000 COVID-19-related tweets collected in the early stages of the pandemic, from February to March 2020. The tweets have been labeled into positive, negative, and neutral sentiment classes. We analyzed the collected tweets for sentiment classification using different sets of features and classifiers. Negative opinion played an important role in conditioning public sentiment, for instance, we observed that people favored lockdown earlier in the pandemic; however, as expected, sentiment shifted by mid-March. Our study supports the view that there is a need to develop a proactive and agile public health presence to combat the spread of negative sentiment on social media following a pandemic
Computer security · Data science · Geography · Internet privacy · Misinformation · Moderation · Pandemic · Political science · Politics · Public opinion · Sentiment analysis · Social media · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Medicine · Misinformation and Its Impacts · Psychology · Sentiment Analysis and Opinion Mining · Social Psychology · Artificial Intelligence
Covid tweet analysis using NLP
Analysis of the evolving factors of social media users’ emotions and behaviors
Health as Battlefield
Changes in Doctor–Patient Relationships in China during Covid-19
Comparison of machine learning algorithms for content based personality resolution of tweets
Misinformation dissemination on social media
Automated Disaster Monitoring From Social Media Posts Using AI-Based Location Intelligence and Sentiment Analysis
Covid-19 Vaccine Side Effect Analysis by Leveraging Social Media
Integration of Neural Architecture Search With Fuzzy Deep Neural Network Model for Emotion AI in Public Health Emergencies
Atmosphere kamaal ka tha (Was Wonderful)
A Hybrid Deep Learning Framework for Hotel Rating Systems
Toward a Cognitive-Inspired Hashtag Recommendation for Twitter Data Analysis
Cyberbullying Detection Using PCA Extracted GLOVE Features and RoBertaNet Transformer Learning Model
A Benchmark of Microvideos for Public Opinion Analysis
Deep Explainable Hate Speech Active Learning on Social-Media Data
Discovering Latent Topics of Digital Technologies From Venture Activities Using Structural Topic Modeling
RHMD
Sustainable Covid-19 Policy Responses With Urban Mobility Network Epidemic Models
Analysis of Public Sentiment on Covid-19 Mitigation Measures in Social Media in the United States Using Machine Learning
Analysis of Public Sentiment on Covid-19 Vaccination Using Twitter
Covid-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
Analysis of network public opinion on Covid-19 epidemic based on the WSR theory
Modeling approaches for early warning and monitoring of pandemic situations as well as decision support
Quantified multidimensional public sentiment characteristics on social media for public opinion management
A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government
Underneath social media texts
Perceived oppression and online support for Covid-19 non-compliance
Social resilience through text analytics of social media
Effect of Social Media Posts on Stock Market During Covid-19 Infodemic
Bert-deep CNN
Sentiment prediction model in social media data using beluga dodger optimization-based ensemble classifier
Impact of Covid-19 on Indian politics
Instagram as a research tool for examining tobacco-related content
Analysis and mining of an election-based network using large-scale twitter data
An augmented multilingual Twitter dataset for studying the Covid-19 infodemic
Sentiment analysis using Twitter data
| Unique citing works | 36 |
|---|---|
| Citations per year | 7,2 |
| Citation span | 2021 - 2026 (6) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 35 |