scholarly journals Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter

2020 ◽  
pp. 1-8
Author(s):  
Sameh N. Saleh ◽  
Christoph U. Lehmann ◽  
Samuel A. McDonald ◽  
Mujeeb A. Basit ◽  
Richard J. Medford
Keyword(s):  
Language Processing ◽  
Public Perception ◽  
Large Scale ◽  
Community Support ◽  
Cross Sectional Study ◽  
Public Understanding ◽  
Cross Sectional ◽  
Social Distancing ◽  
English Only ◽  
Twitter Users

Abstract Objective: Social distancing policies are key in curtailing severe acute respiratory coronavirus virus 2 (SARS-CoV-2) spread, but their effectiveness is heavily contingent on public understanding and collective adherence. We studied public perception of social distancing through organic, large-scale discussion on Twitter. Design: Retrospective cross-sectional study. Methods: Between March 27 and April 10, 2020, we retrieved English-only tweets matching two trending social distancing hashtags, #socialdistancing and #stayathome. We analyzed the tweets using natural language processing and machine-learning models, and we conducted a sentiment analysis to identify emotions and polarity. We evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and associated sentiments. Results: We studied a sample of 574,903 tweets. For both hashtags, polarity was positive (mean, 0.148; SD, 0.290); only 15% of tweets had negative polarity. Tweets were more likely to be objective (median, 0.40; IQR, 0–0.6) with ~30% of tweets labeled as completely objective (labeled as 0 in range from 0 to 1). Approximately half of tweets (50.4%) primarily expressed joy and one-fifth expressed fear and surprise. Each correlated well with topic clusters identified by frequency including leisure and community support (ie, joy), concerns about food insecurity and quarantine effects (ie, fear), and unpredictability of coronavirus disease 2019 (COVID-19) and its implications (ie, surprise). Conclusions: Considering the positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms, we concluded that Twitter users generally supported social distancing in the early stages of their implementation.

scholarly journals 486. Understanding Public Perception of COVID-19 Social Distancing on Twitter

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S309-S309
Author(s):  
Sameh N Saleh ◽  
Christoph Lehmann ◽  
Samuel McDonald ◽  
Mujeeb Basit ◽  
Richard J Medford
Keyword(s):  
Language Processing ◽  
Topic Modeling ◽  
Public Perception ◽  
Large Scale ◽  
Statistical Significance ◽  
Leisure Activities ◽  
Community Support ◽  
Social Distancing ◽  
Positive Sentiment ◽  
Twitter Users

Abstract Background Managing and changing public opinion and behavior are vital for social distancing to successfully slow transmission of COVID-19, preserve hospital resources, and prevent overwhelming the healthcare system’s resources. We sought to leveraging organic, large-scale discussion on Twitter about social distancing to understand public’s beliefs and opinions on this policy. Methods Between March 27 and April 10, 2020, we sampled 574,903 English tweets that matched the two most trending social distancing hashtags at the time, #socialdistancing and #stayathome. We used natural language processing techniques to conduct a sentiment analysis that identifies tweet polarity and emotions. We also evaluated the subjectivity of tweets and estimated the frequency of discussion of social distancing rules. We then identified clusters of discussion using topic modeling and compared the sentiment by topic. Results There was net positive sentiment toward both #socialdistancing and #stayathome with mean sentiment scores of 0.150 (standard deviation [SD], 0.292) and 0.144 (SD, 0.287) respectively. Tweets were also more likely to be objective (median, 0.40; IQR, 0 to 0.6) with approximately 30% of all tweets labeled as completely objective. Approximately half (50.4%) of all tweets primarily expressed joy and one-fifth expressed fear and surprise each (Figure 1). These trends correlated well with topic clusters identified by frequency including leisure activities and community support (i.e., joy), concerns about food insecurity and effects of the quarantine (i.e., fear), and unpredictability of COVID and its unforeseen implications (i.e., surprise) (Table 1). Table 1. Topic clusters identified by topic modeling. Words contributing to the model are shown in decreasing order of weighting. The topics are labeled manually based on these words. The number of tweets primarily with that topic, mean sentiment, mean subjectivity, and sample tweets are also included. Figure 1. Emotion analysis for all tweets and stratified by tweets with the hashtag #socialdistancing and #stayathome. Comparison between the two hashtags is done using Chi-squared testing. Bonferroni correction was used to define statistical significance at a threshold of p = 0.008 (0.05/n, where n = 6 since 6 comparisons were completed). Conclusion The positive sentiment, preponderance of objective tweets, and topics supporting coping mechanisms led us to believe that Twitter users generally supported social distancing measures in the early stages of their implementation. Disclosures All Authors: No reported disclosures

scholarly journals Science, healthcare system, and government effectiveness perception and COVID-19 vaccination acceptance and hesitancy in a global sample: an analytical cross-sectional analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e049716
Author(s):  
Timothy D Dye ◽  
Monica Barbosu ◽  
Shazia Siddiqi ◽  
José G Pérez Ramos ◽  
Hannah Murphy ◽  
...  
Keyword(s):  
Language Processing ◽  
Public Perception ◽  
Strong Predictor ◽  
Cross Sectional Study ◽  
Amazon Mechanical Turk ◽  
Cross Sectional ◽  
Vaccine Acceptance ◽  
Vaccination Acceptance ◽  
Sectional Analysis

BackgroundDeterminants of COVID-19 vaccine acceptance are complex; how perceptions of the effectiveness of science, healthcare and government impact personal COVID-19 vaccine acceptance is unclear, despite all three domains providing critical roles in development, funding and provision, and distribution of COVID-19 vaccine.ObjectiveTo estimate impact of perception of science, healthcare systems, and government along with sociodemographic, psychosocial, and cultural characteristics on vaccine acceptance.DesignWe conducted a global nested analytical cross-sectional study of how the perceptions of healthcare, government and science systems have impacted COVID-19 on vaccine acceptance.SettingGlobal Facebook, Instagram and Amazon Mechanical Turk (mTurk) users from 173 countries.Participants7411 people aged 18 years or over, and able to read English, Spanish, Italian, or French.MeasurementsWe used Χ2 analysis and logistic regression-derived adjusted Odds Ratios (aORs) and 95% CIs to evaluate the relationship between effectiveness perceptions and vaccine acceptance controlling for other factors. We used natural language processing and thematic analysis to analyse the role of vaccine-related narratives in open-ended explanations of effectiveness.ResultsAfter controlling for confounding, attitude toward science was a strong predictor of vaccine acceptance, more so than other attitudes, demographic, psychosocial or COVID-19-related variables (aOR: 2.1; 95% CI: 1.8 to 2.5). The rationale for science effectiveness was dominated by vaccine narratives, which were uncommon in other domains.LimitationsThis study did not include participants from countries where Facebook and Amazon mTurk are not available, and vaccine acceptance reflected intention rather than actual behaviour.ConclusionsAs our findings show, vaccine-related issues dominate public perception of science’s impact around COVID-19, and this perception of science relates strongly to the decision to obtain vaccination once available.

scholarly journals Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study (Preprint)

2020 ◽  
Author(s):  
Chunyan Zhang ◽  
Songhua Xu ◽  
Zongfang Li ◽  
Shunxu Hu
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Language Processing ◽  
Large Scale ◽  
Cross Sectional Study ◽  
Health Economy ◽  
Sectional Study ◽  
Cross Sectional ◽  
Population Groups ◽  
Twitter Data

BACKGROUND Since the beginning of the COVID-19 pandemic in late 2019, its far-reaching impacts have been witnessed globally across all aspects of human life, such as health, economy, politics, and education. Such widely penetrating impacts cast significant and profound burdens on all population groups, incurring varied concerns and sentiments among them. OBJECTIVE This study aims to identify the concerns, sentiments, and disparities of various population groups during the COVID-19 pandemic through a cross-sectional study conducted via large-scale Twitter data mining infoveillance. METHODS This study consisted of three steps: first, tweets posted during the pandemic were collected and preprocessed on a large scale; second, the key population attributes, concerns, sentiments, and emotions were extracted via a collection of natural language processing procedures; third, multiple analyses were conducted to reveal concerns, sentiments, and disparities among population groups during the pandemic. Overall, this study implemented a quick, effective, and economical approach for analyzing population-level disparities during a public health event. The source code developed in this study was released for free public use at GitHub. RESULTS A total of 1,015,655 original English tweets posted from August 7 to 12, 2020, were acquired and analyzed to obtain the following results. Organizations were significantly more concerned about COVID-19 (odds ratio [OR] 3.48, 95% CI 3.39-3.58) and expressed more fear and depression emotions than individuals. Females were less concerned about COVID-19 (OR 0.73, 95% CI 0.71-0.75) and expressed less fear and depression emotions than males. Among all age groups (ie, ≤18, 19-29, 30-39, and ≥40 years of age), the attention ORs of COVID-19 fear and depression increased significantly with age. It is worth noting that not all females paid less attention to COVID-19 than males. In the age group of 40 years or older, females were more concerned than males, especially regarding the economic and education topics. In addition, males 40 years or older and 18 years or younger were the least positive. Lastly, in all sentiment analyses, the sentiment polarities regarding political topics were always the lowest among the five topics of concern across all population groups. CONCLUSIONS Through large-scale Twitter data mining, this study revealed that meaningful differences regarding concerns and sentiments about COVID-19-related topics existed among population groups during the study period. Therefore, specialized and varied attention and support are needed for different population groups. In addition, the efficient analysis method implemented by our publicly released code can be utilized to dynamically track the evolution of each population group during the pandemic or any other major event for better informed public health research and interventions.

scholarly journals Understanding Concerns, Sentiments, and Disparities Among Population Groups During the COVID-19 Pandemic Via Twitter Data Mining: Large-scale Cross-sectional Study

10.2196/26482 ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. e26482
Author(s):  
Chunyan Zhang ◽  
Songhua Xu ◽  
Zongfang Li ◽  
Shunxu Hu
Keyword(s):  
Public Health ◽  
Data Mining ◽  
Language Processing ◽  
Large Scale ◽  
Cross Sectional Study ◽  
Health Economy ◽  
Sectional Study ◽  
Cross Sectional ◽  
Population Groups ◽  
Twitter Data

Background Since the beginning of the COVID-19 pandemic in late 2019, its far-reaching impacts have been witnessed globally across all aspects of human life, such as health, economy, politics, and education. Such widely penetrating impacts cast significant and profound burdens on all population groups, incurring varied concerns and sentiments among them. Objective This study aims to identify the concerns, sentiments, and disparities of various population groups during the COVID-19 pandemic through a cross-sectional study conducted via large-scale Twitter data mining infoveillance. Methods This study consisted of three steps: first, tweets posted during the pandemic were collected and preprocessed on a large scale; second, the key population attributes, concerns, sentiments, and emotions were extracted via a collection of natural language processing procedures; third, multiple analyses were conducted to reveal concerns, sentiments, and disparities among population groups during the pandemic. Overall, this study implemented a quick, effective, and economical approach for analyzing population-level disparities during a public health event. The source code developed in this study was released for free public use at GitHub. Results A total of 1,015,655 original English tweets posted from August 7 to 12, 2020, were acquired and analyzed to obtain the following results. Organizations were significantly more concerned about COVID-19 (odds ratio [OR] 3.48, 95% CI 3.39-3.58) and expressed more fear and depression emotions than individuals. Females were less concerned about COVID-19 (OR 0.73, 95% CI 0.71-0.75) and expressed less fear and depression emotions than males. Among all age groups (ie, ≤18, 19-29, 30-39, and ≥40 years of age), the attention ORs of COVID-19 fear and depression increased significantly with age. It is worth noting that not all females paid less attention to COVID-19 than males. In the age group of 40 years or older, females were more concerned than males, especially regarding the economic and education topics. In addition, males 40 years or older and 18 years or younger were the least positive. Lastly, in all sentiment analyses, the sentiment polarities regarding political topics were always the lowest among the five topics of concern across all population groups. Conclusions Through large-scale Twitter data mining, this study revealed that meaningful differences regarding concerns and sentiments about COVID-19-related topics existed among population groups during the study period. Therefore, specialized and varied attention and support are needed for different population groups. In addition, the efficient analysis method implemented by our publicly released code can be utilized to dynamically track the evolution of each population group during the pandemic or any other major event for better informed public health research and interventions.

scholarly journals Association Between Actual and Perceived U.S. COVID-19 Policies and Preventive Behavior

2021 ◽  
Author(s):  
Meng Li ◽  
Helen Colby
Keyword(s):  
Public Perception ◽  
State Level ◽  
Cross Sectional Study ◽  
Actual State ◽  
Cross Sectional ◽  
Social Distancing ◽  
Gender And Education ◽  
The Usa ◽  
Different Levels ◽  
Better Than

Abstract Background COVID-19 related policies in the USA can be confusing: some states, but not others, implemented mask mandates mid-pandemic, and states reopened their economies to different levels with different timelines after initial shutdowns. Purpose The current research asks: How well does the public’s perception of such policies align with actual policies, and how well do actual versus perceived policies predict the public’s mask-wearing and social distancing behaviors during the COVID-19 pandemic? Methods We conducted a preregistered cross-sectional study among 1,073 online participants who were representative of the U.S. population on age, gender, and education on Monday–Tuesday, July 20–21, 2020. We asked participants which locations they visited in the past weekend, and their mask-wearing and social distancing behaviors at each location. We also measured participants’ beliefs about their state’s policies on mask mandate and business opening and obtained objective measures of these policies from publicly available data. Results Perception about the existence of mask mandate was 91% accurate in states with a mask mandate but only 46% accurate in states without one. Perception of state reopening level did not correlate with policy. It was the perceived but not actual state mask mandate that positively predicted both mask-wearing and social distancing, controlling for state COVID-19 cases, demographic factors, and participants’ numeracy and COVID-19 history. Conclusions The public’s perception of state-level mask mandates erred on the side of assuming there is one. Perception of reopening is almost completely inaccurate. Paradoxically, public perception that a mask mandate exists predicts preventive behaviors better than actual mandates.

Large-Scale Cross-Sectional Study of the Problem Areas in Diabetes Survey among Japanese Patients with Type 2 Diabetes Mellitus

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 2393-PUB
Author(s):  
KENICHIRO TAKAHASHI ◽  
MINORI SHINODA ◽  
RIKA SAKAMOTO ◽  
JUN SUZUKI ◽  
TADASHI YAMAKAWA ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Large Scale ◽  
Japanese Patients ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Diabetes Survey

Gambling-like video game practices: A cross-sectional study of links with problem gambling and disordered gaming

2019 ◽  
Author(s):  
David Zendle
Keyword(s):  
Video Games ◽  
Problem Gambling ◽  
Video Game ◽  
Large Scale ◽  
Significant Degree ◽  
Cross Sectional Study ◽  
Video Gaming ◽  
Cross Sectional ◽  
Streaming Services ◽  
Clinically Significant

A variety of practices have recently emerged which are related to both video games and gambling. Most prominent of these are loot boxes. However, a broad range of other activities have recently emerged which are also related to both gambling and video games: esports betting, real-money video gaming, token wagering, social casino play, and watching videos of both loot box opening and gambling on game streaming services like Twitch.Whilst a nascent body of research has established the robust existence of a relationship between loot box spending and both problem gambling and disordered gaming, little research exists which examines whether similar links may exist for the diverse practices outlined above. Furthermore, no research has thus far attempted to estimate the prevalence of these activities.A large-scale survey of a representative sample of UK adults (n=1081) was therefore conducted in order to investigate these issues. Engagement in all measured forms of gambling-like video game practices were significantly associated with both problem gambling and disordered gaming. An aggregate measure of engagement was associated with both these outcomes to a clinically significant degree (r=0.23 and r=0.43). Engagement in gambling-like video game practices appeared widespread, with a 95% confidence interval estimating that 16.3% – 20.9% of the population engaged in these activities at least once in the last year. Engagement in these practices was highly inter-correlated: Individuals who engaged in one practice were likely to engage in several more.Overall, these results suggest that the potential effects of the blurring of lines between video games and gambling should not primarily be understood to be due to the presence of loot boxes in video games. They suggest the existence of a convergent ecosystem of gambling-like video game practices, whose causal relationships with problem gambling and disordered gaming are currently unclear but must urgently be investigated.

scholarly journals Examination of Construct Validity and Criterion-Related Validity of the German Motor Test in Egyptian Schoolchildren

2021 ◽  
Vol 18 (16) ◽  
pp. 8341
Author(s):  
Osama Abdelkarim ◽  
Julian Fritsch ◽  
Darko Jekauc ◽  
Klaus Bös
Keyword(s):  
Construct Validity ◽  
Physical Fitness ◽  
Large Scale ◽  
Body Height ◽  
Cross Sectional Study ◽  
Latent Factors ◽  
Cross Sectional ◽  
First Order ◽  
Motor Test ◽  
Health Related

Physical fitness is an indicator for children’s public health status. Therefore, the aim of this study was to examine the construct validity and the criterion-related validity of the German motor test (GMT) in Egyptian schoolchildren. A cross-sectional study was conducted with a total of 931 children aged 6 to 11 years (age: 9.1 ± 1.7 years) with 484 (52%) males and 447 (48%) females in grades one to five in Assiut city. The children’s physical fitness data were collected using GMT. GMT is designed to measure five health-related physical fitness components including speed, strength, coordination, endurance, and flexibility of children aged 6 to 18 years. The anthropometric data were collected based on three indicators: body height, body weight, and BMI. A confirmatory factor analysis was conducted with IBM SPSS AMOS 26.0 using full-information maximum likelihood. The results indicated an adequate fit (χ2 = 112.3, df = 20; p < 0.01; CFI = 0.956; RMSEA = 0.07). The χ2-statistic showed significant results, and the values for CFI and RMSEA showed a good fit. All loadings of the manifest variables on the first-order latent factors as well as loadings of the first-order latent factors on the second-order superordinate factor were significant. The results also showed strong construct validity in the components of conditioning abilities and moderate construct validity in the components of coordinative abilities. GMT proved to be a valid method and could be widely used on large-scale studies for health-related fitness monitoring in the Egyptian population.

scholarly journals The Relationships Between Serum DHEA-S and AMH Levels in Infertile Women: A Retrospective Cross-Sectional Study

2021 ◽  
Vol 10 (6) ◽  
pp. 1211
Author(s):  
Li-Te Lin ◽  
Kuan-Hao Tsui
Keyword(s):  
Generalized Linear Models ◽  
Large Scale ◽  
Linear Models ◽  
Positive Association ◽  
Cross Sectional Study ◽  
Dehydroepiandrosterone Sulphate ◽  
Sectional Study ◽  
Cross Sectional ◽  
Infertile Women ◽  
The Relationship

The relationship between serum dehydroepiandrosterone sulphate (DHEA-S) and anti-Mullerian hormone (AMH) levels has not been fully established. Therefore, we performed a large-scale cross-sectional study to investigate the association between serum DHEA-S and AMH levels. The study included a total of 2155 infertile women aged 20 to 46 years who were divided into four quartile groups (Q1 to Q4) based on serum DHEA-S levels. We found that there was a weak positive association between serum DHEA-S and AMH levels in infertile women (r = 0.190, p < 0.001). After adjusting for potential confounders, serum DHEA-S levels positively correlated with serum AMH levels in infertile women (β = 0.103, p < 0.001). Infertile women in the highest DHEA-S quartile category (Q4) showed significantly higher serum AMH levels (p < 0.001) compared with women in the lowest DHEA-S quartile category (Q1). The serum AMH levels significantly increased across increasing DHEA-S quartile categories in infertile women (p = 0.014) using generalized linear models after adjustment for potential confounders. Our data show that serum DHEA-S levels are positively associated with serum AMH levels.

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