Analyzing situational awareness through public opinion to predict adoption of social distancing amid pandemic COVID-19.
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ABSTRACT:
SUBMITTER: Qazi A
PROVIDER: S-EPMC7262187 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundSocial distancing has been implemented by many countries to curb the COVID-19 pandemic. Understanding public support for this policy calls for effective and efficient methods of monitoring public opinion on social distancing. Twitter analysis has been suggested as a cheaper and faster-responding alternative to traditional survey methods. The current empirical evidence is mixed in terms of the correspondence between the two methods.ObjectiveWe aim to compare the two methods in the context of monitoring the Dutch public's opinion on social distancing. For this comparison, we quantified the temporal and spatial variations in public opinion and their sensitivities to critical events using data from both Dutch Twitter users and respondents from a longitudinal survey.MethodsA longitudinal survey on a representative Dutch sample (n = 1,200) was conducted between July and November 2020 to measure opinions on social distancing weekly. From the same period, near 100,000 Dutch tweets were categorized as supporting or rejecting social distancing based on a model trained with annotated data. Average stances for the 12 Dutch provinces and over the 20 weeks were computed from the two data sources and were compared through visualizations and statistical analyses.ResultsBoth data sources suggested strong support for social distancing, but public opinion was much more varied among tweets than survey responses. Both data sources showed an increase in public support for social distancing over time, and a strong temporal correspondence between them was found for most of the provinces. In addition, the survey but not Twitter data revealed structured differences among the 12 provinces, while the two data sources did not correspond much spatially. Finally, stances estimated from tweets were more sensitive to critical events happened during the study period.ConclusionsOur findings indicate consistencies between Twitter data analysis and survey methods in describing the overall stance on social distancing and temporal trends. The lack of spatial correspondence may imply limitations in the data collections and calls for surveys with larger regional samples. For public health management, Twitter analysis can be used to complement survey methods, especially for capturing public's reactivities to critical events amid the current pandemic.
| S-EPMC9366395 | biostudies-literature
Project description:ObjectivesThe purpose of this study is to show that social distancing is a public good under the COVID-19 pandemic.Study designWe apply economic theory to analyse a cross-sectional survey.MethodsEconomic theory is complemented with empirical evidence. An online survey of those aged 30-49 years in Japan (n = 2177) was conducted between April 28 and May 7. Respondents were selected by quota sampling with regard to age group, gender and prefecture of residence. Our main figure shows the proportion of people who increased/did not change/decreased social distancing, relative to the level of altruism and sensitivity to public shaming. The results of OLS and logit models are shown in Supplementary Materials.ResultsSocial distancing is a public good under the COVID-19 pandemic for which the free-rider problem is particularly severe. Altruism and social norms are crucial factors in overcoming this problem. Using an original survey, we show that people with higher altruistic concerns and sensitivity to shaming are more likely to follow social distancing measures.ConclusionsAltruism and social norms are important for reducing the economic cost of the pandemic.
| S-EPMC7425541 | biostudies-literature
Project description:ObjectivePublic crises seriously affect social stability and personal health. When individuals are in a public crisis environment, they will have the impulse and intention to share information, which is a behavioral attitude shown in the face of a crisis. Public crisis information sharing intention will be affected by many factors. This study aims to examine how the process of social presence may influence information sharing intentions during a public crisis and the mediating effects of situational pressure, including risk perception of disease infection and consistency of perception of opinion climate.MethodsThis was a cross-sectional study with 505 youth SNS users. In order to collect as suitable samples as possible, a research website was commissioned to conduct a questionnaire in the early stages of the COVID-19 pandemic in China. This questionnaire was utilized to measure social presence, risk perception of disease infection, consistency of perception of opinion climate and intention to share information about COVID-19. Structural equation modeling was used to examine variable relationships in the research model.ResultsThe results showed that social presence was significantly and positively associated with risk perception of disease infection (B = 0.42, p < 0.001), consistency of perception of opinion climate (B = 0.43, p < 0.001) and intention to share information about COVID-19 (B = 0.48, p < 0.001). Risk perception of disease infection (B = 0.19, p < 0.001) and consistency of perception of opinion climate (B = 0.18, p = 0.002) positively predicted youth SNS users' intention to share information about COVID-19. Risk perception of disease infection and consistency of perception of opinion climate mediated the relationship between social presence and intention to share information about COVID-19 (Z = 2.66, CI: 0.03, 0.15; Z = 2.66, CI: 0.02, 0.16).ConclusionThe study further deepens our understanding of the mechanisms underlying social presence and information sharing intentions. These new findings suggest that some situational cues, including media environment factors (social presence) and perceived stress factors (risk perception of disease infection, consistency of perception of opinion climate) may influence information sharing intention. From the perspective of communication psychology, this study enriched the assessment of information sharing on social media and contributes to understanding of social presence and situation pressure, and it helps to provide specific references for effectively promoting netizens' intention to share information about public crises.
| S-EPMC10367105 | biostudies-literature
Project description:Highlights•Crises present vexing challenges. Understanding what is happening and what therefore to do is for executive leaders.•The POP-DOC Loop links critical situational awareness to appropriate decisions and actions.•The POP-DOC Loop integrates and sequences dynamic functions to enhance leader adaptability and productivity in crisis.•The POP-DOC Loop steps reflect understanding of cognitive functioning and its influence on leader behavior.
| S-EPMC7264025 | biostudies-literature
Project description:ObjectiveSocial 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.DesignRetrospective cross-sectional study.MethodsBetween 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.ResultsWe 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).ConclusionsConsidering 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.
| S-EPMC7450231 | biostudies-literature
Project description:We study partisan differences in Americans' response to the COVID-19 pandemic. Political leaders and media outlets on the right and left have sent divergent messages about the severity of the crisis, which could impact the extent to which Republicans and Democrats engage in social distancing and other efforts to reduce disease transmission. We develop a simple model of a pandemic response with heterogeneous agents that clarifies the causes and consequences of heterogeneous responses. We use location data from a large sample of smartphones to show that areas with more Republicans engaged in less social distancing, controlling for other factors including public policies, population density, and local COVID cases and deaths. We then present new survey evidence of significant gaps at the individual level between Republicans and Democrats in self-reported social distancing, beliefs about personal COVID risk, and beliefs about the future severity of the pandemic.
| S-EPMC7409721 | biostudies-literature
Project description:In many Western societies there are rising concerns about increasing polarization in public debate. However, statistics on private attitudes paint a different picture: the average attitudes in societies are more moderate and remain rather stable over time. The present paper presents an agent-based model of how such discrepancies between public opinion and private attitudes develop at the scale of micro-societies. Based on social psychological theorizing, the model distinguishes between two types of agents: a) those seeking to gain or maintain a good reputation and status, and b) those seeking to promote group harmony by reaching consensus. We characterized these different types of agents by different decision rules for either voicing their opinion or remaining silent, based on the behavior of their proximal network. Results of the model simulations show that even when the private attitudes of the agents are held constant, publicly expressed opinions can oscillate and (depending on the reputational concerns of individual actors) situations can occur in which minorities as well as majorities are silenced. We conclude that the macro-level consequences of micro-level decisions to either voice an opinion or remain silent provide a foundation for better understanding how public opinions are shaped. Moreover, we discuss the conditions under which public opinion could be considered a good representation of private attitudes in a society.
| S-EPMC7688181 | biostudies-literature
Project description:ObjectivesDuring the 6 months since the first coronavirus disease 2019 (COVID-19) patient was diagnosed in Korea on January 20, 2020, various prevention and control measures have been implemented according to the COVID-19 epidemic pattern. Therefore, this study aimed to estimate the reproductive numbers (R) for each epidemic stage to analyze the effects of the preventive measures and to predict the COVID-19 transmission trends.MethodsWe estimated the transmission rates for each epidemic stage by fitting a COVID-19 transmission model, based on a deterministic mathematical model, to the data on confirmed cases. The effects of preventive measures such as social distancing by time period were analyzed, and the size and trends of future COVID-19 outbreaks were estimated.ResultsThe value of R was 3.53 from February18, 2020 to February 28, 2020, and the mean R reduced to 0.45 from March 14, 2020 to April 29, 2020, but it significantly increased to 2.69 from April 30, 2020 to May13, 2020 and it was maintained at 1.03 from May 14, 2020 to July 23, 2020.ConclusionsAccording to the estimated R, it had fallen to below 1 and was maintained at that level owing to the isolation of infected persons by the public health authorities and social distancing measures followed by the general public. Then, the estimated R increased rapidly as the contact among individuals increased during the long holiday period from April 30, 2020 to May 5, 2020. Thereafter, the value of R dropped, with the continued use of preventive measures but remained higher than 1.00, indicating that the COVID-19 outbreak can be prolonged and develop into a severe outbreak at any time.
| S-EPMC7871167 | biostudies-literature
Project description:Social distancing has been a critical public health measure for the COVID-19 pandemic, yet a long history of research strongly suggests that loneliness and social isolation play a major role in several cognitive health issues. What is the true severity and extent of risks involved and what are potential approaches to balance these competing risks? This review aimed to summarize the neurological context of social isolation and loneliness in population health and the long-term effects of social distancing as it relates to neurocognitive aging, health, and Alzheimer's disease and related dementias. The full scope of the underlying causal mechanisms of social isolation and loneliness in humans remains unclear partly because its study is not amenable to randomized controlled trials; however, there are many detailed experimental and observational studies that may provide a hypothesis-generating theoretical framework to better understand the pathophysiology and underlying neurobiology. To address these challenges and inform future studies, we conducted a topical review of extant literature investigating associations of social isolation and loneliness with relevant biological, cognitive, and psychosocial outcomes, and provide recommendations on how to approach the need to fill key knowledge gaps in this important area of research.
| S-EPMC8305633 | biostudies-literature
Project description:The time-varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks; however, delays between infection and reporting of cases hinder its accurate estimation in real-time. A number of nowcasting methods, leveraging available information on data consolidation delays, have been proposed to mitigate this problem. In this work, we retrospectively validate the use of a nowcasting algorithm during 18 months of the COVID-19 pandemic in Italy by quantitatively assessing its performance against standard methods for the estimation of R. Nowcasting significantly reduced the median lag in the estimation of R from 13 to 8 days, while concurrently enhancing accuracy. Furthermore, it allowed the detection of periods of epidemic growth with a lead of between 6 and 23 days. Nowcasting augments epidemic awareness, empowering better informed public health responses.
| S-EPMC11371679 | biostudies-literature