Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set

JMIR Public Health and Surveillance, Journal Year: 2020, Volume and Issue: 6(2), P. e19273 - e19273

Published: May 19, 2020

At the time of this writing, novel coronavirus (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources and economies around world. Social distancing measures, travel bans, self-quarantines, business closures are changing very fabric societies worldwide. With people forced out public spaces, much conversation about these phenomena now occurs online, e.g., social media platforms like Twitter. In paper, we describe a multilingual Twitter dataset that have been continuously collecting since January 22, 2020. We making our available to research community (https://github.com/echen102/COVID-19-TweetIDs). It is hope contribution will enable study online dynamics in context planetary-scale epidemic unprecedented proportions implications. This could also help track scientific misinformation unverified rumors, or understanding fear panic -- undoubtedly more. Ultimately, may contribute towards enabling informed solutions prescribing targeted policy interventions fight global crisis.

Language: Английский

---

Mapping the landscape of Artificial Intelligence applications against COVID-19

Journal of Artificial Intelligence Research, Journal Year: 2020, Volume and Issue: 69, P. 807 - 845

Published: Nov. 19, 2020

COVID-19, the disease caused by SARS-CoV-2 virus, has been declared a pandemic World Health Organization, which reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects COVID-19 crisis. We have identified applications that address challenges posed at different scales, including: molecular, identifying new or existing drugs for treatment; clinical, supporting diagnosis and evaluating prognosis based on medical imaging non-invasive measures; societal, tracking both epidemic accompanying infodemic multiple data sources. also review datasets, tools, resources needed facilitate Intelligence research, discuss strategic considerations related operational implementation multidisciplinary partnerships open science. highlight need international cooperation maximize potential AI in future pandemics.

Language: Английский

---

Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

JMIR Public Health and Surveillance, Journal Year: 2020, Volume and Issue: 6(4), P. e21978 - e21978

Published: Oct. 25, 2020

Background COVID-19 is a scientifically and medically novel disease that not fully understood because it has yet to be consistently deeply studied. Among the gaps in research on outbreak, there lack of sufficient infoveillance data. Objective The aim this study was increase understanding public awareness pandemic trends uncover meaningful themes concern posted by Twitter users English language during pandemic. Methods Data mining conducted collect total 107,990 tweets related between December 13 March 9, 2020. analyses included frequency keywords, sentiment analysis, topic modeling identify explore discussion topics over time. A natural processing approach latent Dirichlet allocation algorithm were used most common tweet as well categorize clusters based keyword analysis. Results results indicate three main aspects regarding First, trend spread symptoms can divided into stages. Second, analysis showed people have negative outlook toward COVID-19. Third, modeling, relating outbreak categories: emergency, how control COVID-19, reports Conclusions Sentiment produce useful information about social media alternative perspectives investigate crisis, which created considerable awareness. This shows good communication channel for both These findings help health departments communicate alleviate specific concerns disease.

Language: Английский

---

Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach

Journal of Medical Internet Research, Journal Year: 2020, Volume and Issue: 22(11), P. e20550 - e20550

Published: Oct. 28, 2020

Background It is important to measure the public response COVID-19 pandemic. Twitter an data source for infodemiology studies involving monitoring. Objective The objective of this study examine COVID-19–related discussions, concerns, and sentiments using tweets posted by users. Methods We analyzed 4 million messages related pandemic a list 20 hashtags (eg, “coronavirus,” “COVID-19,” “quarantine”) from March 7 April 21, 2020. used machine learning approach, Latent Dirichlet Allocation (LDA), identify popular unigrams bigrams, salient topics themes, in collected tweets. Results Popular included “virus,” “lockdown,” “quarantine.” bigrams “stay home,” “corona virus,” “social distancing,” “new cases.” identified 13 discussion categorized them into 5 different themes: (1) health measures slow spread COVID-19, (2) social stigma associated with (3) news, cases, deaths, (4) United States, (5) rest world. Across all topics, dominant were anticipation that can be taken, followed mixed feelings trust, anger, fear topics. revealed significant feeling when people discussed new cases deaths compared other Conclusions This showed approaches leveraged study, enabling research evolving discussions during As situation rapidly evolves, several are consistently on Twitter, such as confirmed death rates, preventive measures, authorities government policies, stigma, negative psychological reactions fear). Real-time monitoring assessment concerns could provide useful emergency responses planning. Pandemic-related fear, mental already evident may continue influence trust second wave occurs or there surge current

Language: Английский

---

A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19

Applied Intelligence, Journal Year: 2020, Volume and Issue: 50(11), P. 3913 - 3925

Published: July 6, 2020

In the past few months, several works were published in regards to dynamics and early detection of COVID-19 via mathematical modeling Artificial intelligence (AI). The aim this work is provide research community with comprehensive overview methods used these studies as well a compendium available open source datasets COVID-19. all, 61 journal articles, reports, fact sheets, websites dealing studied reviewed. It was found that most done based on Susceptible-Exposed-Infected-Removed (SEIR) Susceptible-infected-recovered (SIR) models while AI implementations Convolutional Neural Network (CNN) X-ray CT images. terms datasets, they include aggregated case medical images, management strategies, healthcare workforce, demography, mobility during outbreak. Both Mathematical have both shown be reliable tools fight against pandemic. Several concerning also been collected shared source. However, much needed diversification datasets. Other applications should explored

Language: Английский

---

An exploratory study of COVID-19 misinformation on Twitter

Online Social Networks and Media, Journal Year: 2021, Volume and Issue: 22, P. 100104 - 100104

Published: Feb. 21, 2021

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into propagation, authors and content of misinformation on Twitter around topic order to gain early insights. collected all tweets mentioned verdicts fact-checked claims related over 92 professional fact-checking organisations between January mid-July 2020 share corpus with community. This resulted 1500 relating 1274 false 226 partially claims, respectively. Exploratory analysis author accounts revealed that verified twitter handle(including Organisation/celebrity) are also involved either creating(new tweets) or spreading(retweet) Additionally, we found propagate faster than claims. Compare background tweets, more often concerned discrediting other information media. Authors use less tentative language appear be driven concerns potential harm others. Our results enable us suggest gaps current coverage propose actions authorities users counter

Language: Английский

---

COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies

JMIR Public Health and Surveillance, Journal Year: 2021, Volume and Issue: 7(11), P. e30642 - e30642

Published: Oct. 12, 2021

False claims about COVID-19 vaccines can undermine public trust in ongoing vaccination campaigns, posing a threat to global health. Misinformation originating from various sources has been spreading on the web since beginning of pandemic. Antivaccine activists have also begun use platforms such as Twitter promote their views. To properly understand phenomenon vaccine hesitancy through lens social media, it is great importance gather relevant data.In this paper, we describe data set posts and accounts that publicly exhibit strong antivaccine stance. The made available research community via our AvaxTweets GitHub repository. We characterize collected terms prominent hashtags, shared news sources, most likely political leaning.We started collection October 18, 2020, leveraging streaming application programming interface (API) follow specific antivaccine-related keywords. Then, historical tweets engaged antivaccination narratives between 2020 December Academic Track API. leaning was estimated by measuring bias media outlets they shared.We gathered two curated collections them available: (1) keyword-centered with more than 1.8 million tweets, (2) account-level 135 tweets. lean right (conservative) direction spectrum. fueled misinformation websites already questionable credibility.The vaccine-related may exacerbate levels hesitancy, hampering progress toward vaccine-induced herd immunity, could potentially increase number infections related new variants. For these reasons, understanding paramount importance. Because access first obstacle attain goal, published be used studying enable better hesitancy.

Language: Английский

---

Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Journal of Medical Internet Research, Journal Year: 2020, Volume and Issue: 22(10), P. e22624 - e22624

Published: Sept. 26, 2020

Background With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users express their concerns, opinions, feelings about pandemic. Individuals, health agencies, governments are using communicate COVID-19. Objective The aims of this study were examine key themes topics English-language COVID-19–related tweets posted by individuals explore trends variations how tweets, topics, associated sentiments changed over a period time from before after disease was declared Methods Building emergent stream studies examining English, we performed temporal assessment covering January 1 May 9, 2020, examined tweet sentiment scores uncover trends. Combining data two publicly available sets with those obtained our own search, compiled set 13.9 million individuals. We use guided latent Dirichlet allocation (LDA) infer underlying used VADER (Valence Aware Dictionary sEntiment Reasoner) analysis compute weekly 17 weeks. Results Topic modeling yielded 26 which grouped into 10 broader tweets. Of 13,937,906 2,858,316 (20.51%) impact economy markets, followed spread growth cases (2,154,065, 15.45%), treatment recovery (1,831,339, 13.14%), care sector (1,588,499, 11.40%), response (1,559,591, 11.19%). Average compound found be negative throughout cases, symptoms, racism, source outbreak, political In contrast, saw reversal positive prevention, government response, industry, recovery. Conclusions Identification dominant themes, sentiments, changing pandemic can help governments, policy makers frame appropriate responses prevent control

Language: Английский

---

COVID-19 open source data sets: a comprehensive survey

Applied Intelligence, Journal Year: 2020, Volume and Issue: 51(3), P. 1296 - 1325

Published: Sept. 21, 2020

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. early 2020, spread all continents world except Antarctica, causing widespread infections and deaths due to its contagious characteristics no medically proven treatment. The pandemic has been termed as most consequential global crisis since World Wars. first line defense against are non-pharmaceutical measures like social distancing personal hygiene. great affecting billions lives economically socially motivated scientific community come up with solutions based on computer-aided digital technologies for diagnosis, prevention, estimation COVID-19. Some these efforts focus statistical Artificial Intelligence-based analysis available data concerning All necessitate that brought service should be open source promote extension, validation, collaboration work fight pandemic. Our survey is by can mainly categorized

Language: Английский

---

COVIDLies: Detecting COVID-19 Misinformation on Social Media

Published: Jan. 1, 2020

The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on internet, specifically social media such as Twitter. However, due novel language and rapid change of information, existing detection datasets are not effective evaluating systems designed detect this topic. Misinformation can be divided into two sub-tasks: (i) retrieval misconceptions relevant posts being checked veracity, (ii) stance identify whether Agree, Disagree, or express No Stance towards retrieved misconceptions. To facilitate research task, we release COVIDLies (https://ucinlp.github.io/covid19 ), a dataset 6761 expert-annotated tweets evaluate performance 86 different pieces COVID-19 related misinformation. We NLP dataset, providing initial benchmarks identifying key challenges future models improve upon.

Language: Английский

---