Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach

Hunt, Kyle; Agarwal, Puneet; Zhuang, Jun

doi:10.1111/risa.13634

Cited by 38 publications

(18 citation statements)

References 54 publications

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“…22 Of note, this report suggests that the rates of infection in NHS staff are not as high as some previous estimates reported from England, notably social medial reports of seroprevalence up to 60%. 23 Studies have estimated seroprevalence to be between 15% and 45% in HCWs in patient-facing roles and 3%-25% overall. 21 24-26 These rates are thought to vary by specialty, 27 but the infection rates between different subgroups vary by report.…”

Section: Hcw Infectionmentioning

confidence: 99%

National cross-sectional survey of 1.14 million NHS staff SARS-CoV-2 serology tests: a comparison of NHS staff with regional community seroconversion rates

et al. 2021

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ObjectivesAn initial report of findings from 1.14 million SARS CoV-2 serology tests in National Health Service (NHS) staff to compare NHS staff seroconversion with community seroconversion rates at a regional level.DesignA national cross-sectional survey.SettingA SARS-CoV-2 antibody testing programme offered across all NHS Trusts.Participants1.14 million NHS staff.InterventionSARS-CoV-2 antibody testing.Primary and secondary outcome measuresSARS-CoV-2 antibody testing was used to estimate the seroprevalence of SARS-CoV-2 in NHS staff by region, compared with community seroprevalence as determined by the COVID-19 Infection Survey (Office for National Statistics). We also explored seroprevalence trends by regional COVID-19 activity, using regional death rates as a proxy for COVID-19 ‘activity’.Results1 146 310 tests were undertaken on NHS staff between 26 May and 31 August 2020. 186 897 NHS tests were positive giving a seroconversion rate of 16.3% (95% CI 16.2% to 16.4%), in contrast to the national community seroconversion rate of 5.9% (95% CI 5.3% to 6.6%). There was significant geographical regional variation, which mirrored the trends seen in community prevalence rates. NHS staff were infected at a higher rate than the general population (OR 3.1, 95% CI 2.8 to 3.5). NHS seroconversion by regional death rate suggested a trend towards higher seroconversion rates in the areas with higher COVID-19 ‘activity’.ConclusionsThis is the first cross-sectional survey assessing the risk of COVID-19 disease in healthcare workers at a national level. It is the largest study of its kind. It suggests that NHS staff have a significantly higher rate of COVID-19 seroconversion compared with the general population in England, with regional variation across the country which matches the background population prevalence trends. There was also a trend towards higher seroconversion rates in areas which had experienced high COVID-19 clinical activity. This work has global significance in terms of the value of such a testing programme and contributing to the understanding of healthcare worker seroconversion at a national level.

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Section: Hcw Infectionmentioning

confidence: 99%

National cross-sectional survey of 1.14 million NHS staff SARS-CoV-2 serology tests: a comparison of NHS staff with regional community seroconversion rates

et al. 2021

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“…In related works a variety of the machine learning algorithms applied on tweets from multiple different events to monitor multiple cases of misinformation simultaneously. From the results of this work the model accuracy of various algorithms such as k-Nearest neighbourhood and Support Vector Machines proved efficient for the purpose [18]. In another work a COVID-19 news verification system developed based on three tier system developed (CoVerifi), a frontend based on React.js, a backend based on Python Machine Learning services and a database storing [19].…”

Section: Discussionmentioning

confidence: 84%

Machine Learning to Identify Fake News for COVID-19

Isaakidou

Zoulias

Diomidous

2021

Studies in Health Technology and Informatics

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International Organizations are seriously concerned about the fake news phenomenon. UNESCO has defined the term of misinformation/disinformation, which are the two faces of fake news. European Commission has conducted a survey about “Fake News” through EU citizens to estimate the awareness and people behaviour related to the appearance of fake news and disinformation on electronic. The findings are quite worrying, since about 40% come across fake news daily and 85% evaluate fake news as a problem. The aim of this work is to introduce an Artificial Intelligence approach, the Decision Trees algorithm to identify fake news on the COVID-19.

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“…Covid-19 vaccines, behaviours against them, etc) are already starting to increase and healthcare informatics applications are becoming a very useful tool of defence [7]. Previous works proved that "Machine Learning" application seems very useful in recognising and striking out fake news and other disinformation [8,9,10].…”

Section: Resultsmentioning

confidence: 99%

The Contribution of Informatics to Overcoming the Covid-19 Fake News Outbreak by Learning to Navigate the Infodemic

Isaakidou

Diomidous

2022

Studies in Health Technology and Informatics

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Daily, people are being exposed to an enormous amount of Covid-19 information, but not all of it is reliable. This phenomenon seriously affects the public health policy effectiveness, because there is a lot of misleading or inaccurate information, which is spreading rapidly and makes it more difficult to restrict the pandemic. Healthcare informatics has emerged as a diverse and important new field. Healthcare informatics applications are becoming more and more popular and are providing easy access to new sources of knowledge. This way, the quality of patient care will improve and productivity will increase. However, people should also learn how to navigate this infodemic properly.

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Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach

Cited by 38 publications

References 54 publications

National cross-sectional survey of 1.14 million NHS staff SARS-CoV-2 serology tests: a comparison of NHS staff with regional community seroconversion rates

National cross-sectional survey of 1.14 million NHS staff SARS-CoV-2 serology tests: a comparison of NHS staff with regional community seroconversion rates

Machine Learning to Identify Fake News for COVID-19

The Contribution of Informatics to Overcoming the Covid-19 Fake News Outbreak by Learning to Navigate the Infodemic

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