Design and analysis of a large-scale COVID-19 tweets dataset

Applied Intelligence, Journal Year: 2020, Volume and Issue: 51(5), P. 2790 - 2804

Published: Nov. 6, 2020

Language: Английский

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Political polarization drives online conversations about COVID ‐19 in the United States

Human Behavior and Emerging Technologies, Journal Year: 2020, Volume and Issue: 2(3), P. 200 - 211

Published: June 18, 2020

Since the outbreak in China late 2019, novel coronavirus (COVID-19) has spread around world and come to dominate online conversations. By linking 2.3 million Twitter users locations within United States, we study aggregate how political characteristics of affect evolution discussions about COVID-19. We show that COVID-19 chatter States is largely shaped by polarization. Partisanship correlates with sentiment toward government measures tendency share health prevention messaging. Cross-ideological interactions are modulated user segregation polarized network structure. also observe a correlation between engagement topics related public varying impact disease different U.S. states. These findings may help inform policies both offline. Decision-makers calibrate their use platforms measure effectiveness campaigns, monitor reception national state-level policies, tracking real-time highly social media ecosystem.

Language: Английский

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Effects of urban parks on residents’ expressed happiness before and during the COVID-19 pandemic

Landscape and Urban Planning, Journal Year: 2021, Volume and Issue: 212, P. 104118 - 104118

Published: April 15, 2021

Language: Английский

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Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 95730 - 95753

Published: Jan. 1, 2021

The beginning of 2020 has seen the emergence coronavirus outbreak caused by a novel virus called SARS-CoV-2. sudden explosion and uncontrolled worldwide spread COVID-19 show limitations existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies as blockchain Artificial Intelligence (AI) have emerged promising solutions for fighting epidemic. particular, can combat pandemics enabling early detection outbreaks, ensuring ordering medical data, reliable supply chain during tracing. Moreover, AI provides intelligent identifying symptoms treatments supporting drug manufacturing. Therefore, we present an extensive survey on use combating epidemics. First, introduce new conceptual architecture which integrates COVID-19. Then, latest research efforts various applications. newly emerging projects cases enabled these to deal with pandemic are also presented. A case study is provided using federated detection. Finally, point out challenges future directions that motivate more coronavirus-like

Language: Английский

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Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 23(2), P. e25431 - e25431

Published: Jan. 20, 2021

Background Social media is a rich source where we can learn about people’s reactions to social issues. As COVID-19 has impacted lives, it essential capture how people react public health interventions and understand their concerns. Objective We aim investigate concerns in North America, especially Canada. Methods analyzed COVID-19–related tweets using topic modeling aspect-based sentiment analysis (ABSA), interpreted the results with experts. To generate insights on effectiveness of specific for COVID-19, compared timelines topics discussed timing implementation interventions, synergistically including information aspects our analysis. In addition, further anti-Asian racism, sentiments Asians Canadians. Results Topic identified 20 topics, experts provided interpretations based top-ranked words representative each topic. The interpretation timeline showed that discovered trend are highly related promotions such as physical distancing, border restrictions, handwashing, staying home, face coverings. After training data ABSA human-in-the-loop, obtained 545 aspect terms (eg, “vaccines,” “economy,” “masks”) 60 opinion “infectious” (negative) “professional” (positive), which were used inference key selected by negative overall outbreak, misinformation Asians, positive distancing. Conclusions Analyses natural language processing techniques domain expert involvement produce useful health. This study first analyze Canada comparison United States human-in-the-loop domain-specific ABSA. kind could help agencies well what messages resonating populations who use Twitter, be helpful when designing policy new interventions.

Language: Английский

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Misinformation, manipulation, and abuse on social media in the era of COVID-19

Journal of Computational Social Science, Journal Year: 2020, Volume and Issue: 3(2), P. 271 - 277

Published: Nov. 1, 2020

The COVID-19 pandemic represented an unprecedented setting for the spread of online misinformation, manipulation, and abuse, with potential to cause dramatic real-world consequences. aim this special issue was collect contributions investigating issues such as emergence infodemics, conspiracy theories, automation, harassment on onset coronavirus outbreak. Articles in collection adopt a diverse range methods techniques, focus study narratives that fueled diffusion patterns global news sentiment, hate speech social bot interference, multimodal Chinese propaganda. diversity methodological scientific approaches undertaken aforementioned articles demonstrates interdisciplinarity these issues. In turn, crucial endeavors might anticipate growing trend studies where models, techniques will be combined tackle different aspects abuse.

Language: Английский

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ReCOVery

Published: Oct. 19, 2020

First identified in Wuhan, China, December 2019, the outbreak of COVID-19 has been declared as a global emergency January, and pandemic March 2020 by World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" information low credibility such fake news conspiracies. In work, present ReCOVery, repository designed constructed to facilitate research on combating regarding COVID-19. We first broadly search investigate ~2,000 publishers, from which 60 extreme [high or low] levels credibility. By inheriting media they were published, total 2,029 articles coronavirus, published January May 2020, collected repository, along 140,820 tweets that reveal how these have spread Twitter social network. The provides multimodal including textual, visual, temporal, network information. way is obtained allows trade-off between dataset scalability label accuracy. Extensive experiments conducted data statistics distributions, well provide baseline performances for predicting so future methods can be compared. Our available at http://coronavirus-fakenews.com.

Language: Английский

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The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 33203 - 33223

Published: Jan. 1, 2021

The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related instauration of lockdowns in areas most hit by pandemic. Social media been an important support for people while passing through this difficult period. On November 9, 2020, when first vaccine with more than 90% effective rate announced, social reacted and worldwide have started express their feelings vaccination, was no longer a hypothesis but closer, each day, become reality. present paper aims analyze dynamics opinions regarding COVID-19 vaccination considering one-month period following announcement, until took place UK, civil society manifested higher interest process. Classical machine learning deep algorithms compared select best performing classifier. 2 349 659 tweets collected, analyzed, put connection events reported media. Based on analysis, it can be observed that

Language: Английский

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Self-reported COVID-19 symptoms on Twitter: an analysis and a research resource

Journal of the American Medical Informatics Association, Journal Year: 2020, Volume and Issue: 27(8), P. 1310 - 1315

Published: May 22, 2020

Abstract Objective To mine Twitter and quantitatively analyze COVID-19 symptoms self-reported by users, compare symptom distributions across studies, create a lexicon for future research. Materials Methods We retrieved tweets using COVID-19-related keywords, performed semiautomatic filtering to curate self-reports of positive-tested users. extracted mentioned the mapped them standard concept IDs in Unified Medical Language System, compared those reported early studies from clinical settings. Results identified 203 users who 1002 668 unique expressions. The most frequently-reported were fever/pyrexia (66.1%), cough (57.9%), body ache/pain (42.7%), fatigue (42.1%), headache (37.4%), dyspnea (36.3%) amongst at least 1 symptom. Mild symptoms, such as anosmia (28.7%) ageusia (28.1%), frequently on Twitter, but not studies. Conclusion spectrum may complement

Language: Английский

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Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

Published: Jan. 1, 2021

Firoj Alam, Shaden Shaar, Fahim Dalvi, Hassan Sajjad, Alex Nikolov, Hamdy Mubarak, Giovanni Da San Martino, Ahmed Abdelali, Nadir Durrani, Kareem Darwish, Abdulaziz Al-Homaid, Wajdi Zaghouani, Tommaso Caselli, Gijs Danoe, Friso Stolk, Britt Bruntink, Preslav Nakov. Findings of the Association for Computational Linguistics: EMNLP 2021.

Language: Английский

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