An Infodemiology and Infoveillance Study on COVID-19: Analysis of Twitter and Google Trends

Alshahrani, Reem; Babour, Amal

doi:10.3390/su13158528

Cited by 9 publications

(6 citation statements)

References 29 publications

(35 reference statements)

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“…Google Trends estimated interest agreed with analyses by Mavragani and Gkillas, 22 Turk et al, 40 and Alshahrani and Babour. 23 RF models were used to predict sentiment types. The most important factors for all models were date, COVID-19 cases, COVID-19 deaths, and Google Trends estimated interest.…”

Section: Discussionmentioning

confidence: 99%

“…Google Trends estimated interest agreed with analyses by Mavragani and Gkillas, 22 Turk et al, 40 and Alshahrani and Babour. 23…”

Section: Discussionmentioning

confidence: 99%

“…Turk et al created a predictive model for COVID-19 cases using Google Trends and virtual consultation data. Alshahrani and Babour 23 used Twitter and Google Trends to analyze search behaviors and predict new COVID-19 cases.…”

Section: Literature Reviewmentioning

confidence: 99%

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What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data

Rathke¹,

Yu²,

Huang³

2023

Disaster med. public health prep.

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The rapid onset of COVID-19 created a complex virtual collective consciousness. Misinformation and polarization were hallmarks of the pandemic in the United States, highlighting the importance of studying public opinion online. Humans express their thoughts and feelings more openly than ever before on social media, co-occurrence of multiple data sources becomes valuable for monitoring and understanding public sentimental preparedness and response to an event within our society. In this study, Twitter and Google Trends data were used as the co-occurrence data for the understanding of the dynamics of sentiment and interest during the COVID-19 pandemic in the United States from January 2020 to September 2021. Developmental trajectory analysis of Twitter sentiment was conducted using corpus linguistic techniques and word cloud mapping to reveal eight positive and negative sentiments and emotions. Machine learning algorithms were employed to implement the opinion mining how Twitter sentiment was related to Google Trends interest with historical COVID-19 public health data. The sentiment analysis went beyond polarity to detect specific feelings and emotions during the pandemic. The discoveries on the behaviors of emotions at each stage of the pandemic were presented from the emotion detection when associated with the historical COVID-19 data and Google Trends data.

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Section: Discussionmentioning

confidence: 99%

“…Google Trends estimated interest agreed with analyses by Mavragani and Gkillas, 22 Turk et al, 40 and Alshahrani and Babour. 23…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data

Rathke¹,

Yu²,

Huang³

2023

Disaster med. public health prep.

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“…Also, [99] investigated web search exercises and practices connected with the COVID-19 pandemic from February 20, 2020, to May 6, 2020, using Google Trends and Instagram hashtags. The research examined the names used to distinguish the infection, well-being, and hazard discernment, life during the lockdown, and data connected with the reception of COVID-19 infodemic monikers (query, hashtag, or phrase that generates or feeds fake news, misinterpretations, or discriminatory phenomena).…”

Section: Contribution Of Ai To Covid-19 Epidemiology and Infodemiologymentioning

confidence: 99%

A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis

Isiaka¹,

Babatunde²,

Ajao³

et al. 2022

AIMS Research Publication

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Following the identification of Coronavirus Disease 2019 (COVID-19) in Wuhan, China in December 2019, AI researchers have teamed up with a health specialist to combat the virus. This study explores the medical and non-medical areas of COVID-19 that AI has impacted: the prevalence of the AI technologies adopted across all stages of the pandemic, the collaboration networks of global AI researchers, and the open issues. 21,219 papers from ACM Digital, Science Direct and Google Scholar were examined. Adherence to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and utilizing the PICO (population, intervention, comparison, and outcome) paradigm, guided the inclusion of researches in the review. Tables and graphs were utilized to display the results. Analysis revealed that AI has impacted 4 molecular, 4 clinical, and 7 societal areas of COVID-19. Deep Learning among other AI technologies was traced to all aspects of the pandemic. 2173 authors and co-authors were traced to these achievements, while 32 of the most connected 51 authors were affiliated with institutions in China, 18 to the United States, and 1 to Europe. The open issues identified had to do with the quality of datasets, AI model deployment, and privacy issues. This study demonstrates how AI may be utilized for COVID-19 diagnosis, prediction, medication and vaccine identification, prognosis, and contact person monitoring. This investigation began at the beginning of the epidemic and continued until the first batch of vaccinations received approval. The study provided collaboration opportunities for AI researchers and revealed open issues that will spike further research toward preparing the world for any future pandemic Keywords: Artificial Intelligence, COVID-19, Diagnosis, Prognosis, Contact Tracing

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“…Google mobility data was used to assess the relationship between different community activities and the pandemic transmission rate. There was a strong negative correlation between the number of COVID-19 cases and the number of COVID-19 searches on Google Trends, showing a statistical significance (18). Besides, with the advancement of time-series modeling and prediction, machine learning model can also be used to predict the spread of COVID-19 (19)(20)(21).…”

mentioning

confidence: 97%

Public Risk Perception Explains the Mitigation of COVID-19

Lai

Wang

Cao³

et al. 2023

Preprint

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Background Public awareness of self-protection (PASP) is of vital importance in predicting the spread of infectious diseases. It can change the way people travel and socialize, thereby curbing the spread of the infectious diseases and mitigating its impact. The objective of this study is to explore the impact of PASP on the transmission of COVID-19 and to predict its epidemic trend.Methods Based on large-scale Weibo and Twitter datasets, we analyzes the temporal patterns of PASP for COVID-19 and develop improved models integrating PASP to predict the spread of COVID-19 in both China and UK. Additionally, we implement the models to evaluate non-pharmaceutical intervention strategies such as travel restrictions.Results During the first two months of local outbreaks with mitigation actions, the rate of online users with PASP in China and UK increased by 53% and 26%, respectively. And the integrated models yield an improved\({R^2}\)of 96.57% and 95.12% for predicting outbreaks in China and UK.Conclusions This study presents a new attempt to quantify PASP and extend it to predict the epidemic trend with massive online social media data. And we demonstrate that measuring public response had instructional significance in epidemiological models and is important in infectious disease prevention and control.

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An Infodemiology and Infoveillance Study on COVID-19: Analysis of Twitter and Google Trends

Cited by 9 publications

References 29 publications

What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data

What Remains Now That the Fear Has Passed? Developmental Trajectory Analysis of COVID-19 Pandemic for Co-occurrences of Twitter, Google Trends, and Public Health Data

A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis

Public Risk Perception Explains the Mitigation of COVID-19

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