Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Applied Soft Computing, Journal Year: 2020, Volume and Issue: 101, P. 107057 - 107057

Published: Dec. 26, 2020

Language: Английский

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COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study

JMIR Public Health and Surveillance, Journal Year: 2020, Volume and Issue: 6(2), P. e19374 - e19374

Published: May 5, 2020

Background Since the beginning of novel coronavirus disease (COVID-19) outbreak, fake news and misleading information have circulated worldwide, which can profoundly affect public health communication. Objective We investigated online search behavior related to COVID-19 outbreak attitudes “infodemic monikers” (ie, erroneous that gives rise interpretative mistakes, news, episodes racism, etc) circulating in Italy. Methods By using Google Trends explore internet activity from January March 2020, article titles most read newspapers government websites were mined investigate infodemic monikers across various regions cities Search volume values average peak comparison (APC) used analyze results. Results Keywords such as “novel coronavirus,” “China “COVID-19,” “2019-nCOV,” “SARS-COV-2” top scientific terms trending The five searches “face masks,” “amuchina” (disinfectant), “symptoms “health bulletin,” “vaccines for coronavirus.” Umbria Basilicata recorded a high number (APC weighted total >140). Misinformation was widely Campania region, racism-related widespread Basilicata. These frequently searched >100) more than 10 major Italy, including Rome. Conclusions identified growing regional population-level interest majority amuchina, face masks, bulletins, symptoms. large observed we recommend agencies use predict human well manage misinformation circulation

Language: Английский

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A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis

PLoS ONE, Journal Year: 2021, Volume and Issue: 16(2), P. e0245909 - e0245909

Published: Feb. 25, 2021

The spread of Covid-19 has resulted in worldwide health concerns. Social media is increasingly used to share news and opinions about it. A realistic assessment the situation necessary utilize resources optimally appropriately. In this research, we perform tweets sentiment analysis using a supervised machine learning approach. Identification sentiments from would allow informed decisions for better handling current pandemic situation. dataset extracted Twitter IDs as provided by IEEE data port. Tweets are an in-house built crawler that uses Tweepy library. cleaned preprocessing techniques TextBlob contribution work performance evaluation various classifiers our proposed feature set. This set formed concatenating bag-of-words term frequency-inverse document frequency. classified positive, neutral, or negative. Performance evaluated on accuracy, precision, recall, F 1 score. For completeness, further investigation made Long Short-Term Memory (LSTM) architecture deep model. results show Extra Trees Classifiers outperform all other models achieving 0.93 accuracy score concatenated features LSTM achieves low compared classifiers. To demonstrate effectiveness set, with Vader technique based GloVe extraction

Language: Английский

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Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the “Chinese virus” on Twitter: Quantitative Analysis of Social Media Data

Journal of Medical Internet Research, Journal Year: 2020, Volume and Issue: 22(5), P. e19301 - e19301

Published: April 26, 2020

Stigma is the deleterious, structural force that devalues members of groups hold undesirable characteristics. Since stigma created and reinforced by society-through in-person online social interactions-referencing novel coronavirus as "Chinese virus" or "China has potential to create perpetuate stigma.The aim this study was assess if there an increase in prevalence frequency phrases on Twitter after March 16, 2020, US presidential reference term.Using Sysomos software (Sysomos, Inc), we extracted tweets from United States using a list keywords were derivatives virus." We compared at national state levels posted between 9 15 (preperiod) with those 19 25 (postperiod). used Stata 16 (StataCorp) for quantitative analysis, Python (Python Software Foundation) plot state-level heat map.A total 16,535 identified preperiod, 177,327 postperiod, illustrating nearly ten-fold level. All 50 states witnessed number exclusively mentioning instead disease (COVID-19) coronavirus. On average, 0.38 referencing per 10,000 people level 4.08 these stigmatizing also indicating increase. The 5 highest postperiod Pennsylvania (n=5249), New York (n=11,754), Florida (n=13,070), Texas (n=14,861), California (n=19,442). Adjusting population size, Arizona (5.85), (6.04), (6.09), Nevada (7.72), Wyoming (8.76). largest pre- Kansas (n=697/58, 1202%), South Dakota (n=185/15, 1233%), Mississippi (n=749/54, 1387%), Hampshire (n=582/41, 1420%), Idaho (n=670/46, 1457%).The rise virus," along content tweets, indicate knowledge translation may be occurring COVID-19 likely being perpetuated Twitter.

Language: Английский

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Artificial Intelligence–Enabled Analysis of Public Attitudes on Facebook and Twitter Toward COVID-19 Vaccines in the United Kingdom and the United States: Observational Study

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 23(4), P. e26627 - e26627

Published: Feb. 1, 2021

Background Global efforts toward the development and deployment of a vaccine for COVID-19 are rapidly advancing. To achieve herd immunity, widespread administration vaccines is required, which necessitates significant cooperation from general public. As such, it crucial that governments public health agencies understand sentiments vaccines, can help guide educational campaigns other targeted policy interventions. Objective The aim this study was to develop apply an artificial intelligence–based approach analyze on social media in United Kingdom States better attitude concerns regarding vaccines. Methods Over 300,000 posts related were extracted, including 23,571 Facebook 144,864 States, along with 40,268 tweets 98,385 March 1 November 22, 2020. We used natural language processing deep learning–based techniques predict average sentiments, sentiment trends, topics discussion. These factors analyzed longitudinally geospatially, manual reading randomly selected points interest helped identify underlying themes validated insights analysis. Results Overall averaged positive, negative, neutral at 58%, 22%, 17% Kingdom, compared 56%, 24%, 18% respectively. Public optimism over development, effectiveness, trials as well their safety, economic viability, corporation control identified. our findings those nationwide surveys both countries found them correlate broadly. Conclusions Artificial intelligence–enabled analysis should be considered adoption by institutions alongside conventional methods assessing attitude. Such analyses could enable real-time assessment, scale, confidence trust address sceptics, more effective policies communication strategies maximize uptake.

Language: Английский

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Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models

Journal of Clinical Medicine, Journal Year: 2020, Volume and Issue: 9(10), P. 3350 - 3350

Published: Oct. 19, 2020

The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on people’s daily lives, with strong implications for stress levels due to the threat of contagion restrictions freedom. Given link between high adverse physical mental consequences, COVID-19 pandemic is certainly public health issue. In present study, we assessed effect in N = 2053 Italian adults, characterized more vulnerable individuals basis sociodemographic features stable psychological traits. A set 18 psycho-social variables, generalized regressions, predictive machine learning approaches were leveraged. We identified higher perceived study sample relative normative values. Higher distress found women, participants lower income, living others. rates emotional stability self-control, as well positive coping style internal locus control, emerged protective factors. Predictive models stress, sensitivity greater than 76%. results suggest characterization people who are experiencing during pandemic. This may contribute early targeted intervention strategies.

Language: Английский

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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

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Social Media and Health Care, Part I: Literature Review of Social Media Use by Health Care Providers

Journal of Medical Internet Research, Journal Year: 2021, Volume and Issue: 23(4), P. e23205 - e23205

Published: March 5, 2021

As the world continues to advance technologically, social media (SM) is becoming an essential part of billions people's lives worldwide and affecting almost every industry imaginable. more digitally oriented, health care increasingly visualizing SM as important channel for promotion, employment, recruiting new patients, marketing providers (HCPs), building a better brand name, etc. HCPs are bound ethical principles toward their colleagues, public in digital much real world.This review aims shed light on use discuss how it has been used tool from perspective HCPs.A literature was conducted between March April 2020 using MEDLINE, PubMed, Google Scholar, Web Science all English-language medical studies that were published since 2007 discussed any form care. Studies not English, whose full text accessible, or investigated patients' perspectives excluded this part, reviews pertaining legal considerations use.The initial search yielded 83 studies. More included article references, total 158 reviewed. uses best categorized career development practice recruitment, professional networking destressing, education, telemedicine, scientific research, influencing behavior, issues.Multidimensional care, including pairing with other forms communication, shown be very successful. Striking right balance traditional important.

Language: Английский

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Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags

Journal of Medical Internet Research, Journal Year: 2020, Volume and Issue: 22(8), P. e20673 - e20673

Published: Aug. 3, 2020

Background Although “infodemiological” methods have been used in research on coronavirus disease (COVID-19), an examination of the extent infodemic moniker (misinformation) use internet remains limited. Objective The aim this paper is to investigate search behaviors related COVID-19 and examine circulation monikers through two platforms—Google Instagram—during current global pandemic. Methods We defined as a term, query, hashtag, or phrase that generates feeds fake news, misinterpretations, discriminatory phenomena. Using Google Trends Instagram hashtags, we explored activities pandemic from February 20, 2020, May 6, 2020. investigated names identify virus, health risk perception, life during lockdown, information adoption monikers. computed average peak volume with 95% CI for Results top six COVID-19–related terms searched were “coronavirus,” “corona,” “COVID,” “virus,” “corona virus,” “COVID-19.” Countries higher number cases had queries Google. “coronavirus ozone,” laboratory,” 5G,” conspiracy,” bill gates” widely circulated internet. Searches “tips cures” spiked relation US president speculating about “miracle cure” suggesting injection disinfectant treat virus. Around thirds (n=48,700,000, 66.1%) users hashtags “COVID-19” “coronavirus” disperse virus-related information. Conclusions Globally, there growing interest COVID-19, numerous continue circulate Based our findings, hope encourage mass media regulators organizers be vigilant diminish these decrease spread misinformation.

Language: Английский

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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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