ABSTRACT
As social media such as Twitter has become an important medium for disseminating information, it is essential to understand how the information diffusion on social media influences public adoption of vaccines. Based on the innovation diffusion theory, we construct a user and information quality indicator system for early adopters of COVID-19 vaccination by identifying their creation of user-generated content on social media. Machine learning approaches and text analysis methods are used to perform topic clustering and sentiment analysis on vaccination-related tweets on Twitter. Based on each country's vaccination data in January 2021, the study examines the relationship between the quality of social media early adopters, and the quality of the information they publish with vaccine adoption by using the OSL regression model. The empirical results show that the total number of tests, the number of new COVID-19 cases, and the human development index have a significantly positive influence on vaccine adoption. Neutral emotions and offensive language of early adopters on social media have a significantly negative relationship with vaccine adoption. These interesting findings can help governments and public health officials understand early adopters' perceptions of vaccines and play an important role in targeted policy interventions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
[Objective] This paper tries to measure the netizens' trust in government microblogs during public health emergencies, and then explores reasons for the changes. [Methods] First, we calculated the trust from the comments on government microblogs with the comment objects, the topic similarity between comments and microblogs, as well as their sentiments. Then, we added the numbers of likes and forwards/retweets to decide the comprehensive trust of the netizens toward the government microblogs. [Results] We examined out model with microblog data on COVID-19 and found that topics related to industrial and government efforts fighting the pandemic enhanced the trust in government microblogs. There were great differences in the development trends and reasons of the trust in government microblogs from different fields. [Limitations] We only used the events and the microbloggers as the objects of comments. [Conclusions] The proposed model could help government agencies improve decision making, public trust, and lead online opinion during public health emergencies. © 2022, Chinese Academy of Sciences. All rights reserved.
ABSTRACT
Technical documentation for educational tests focuses primarily on properties of individual scores at single points in time. Reliability, standard errors of measurement, item parameter estimates, fit statistics, and linking constants are standard technical features that external stakeholders use to evaluate items and individual scale scores. However, these cross-sectional, "point-in-time" features can mask threats to the validity of score interpretations, including those for aggregate scores and trends over time. We use test score data collected before and during the COVID-19 pandemic to show that longitudinal analyses, not just point-in-time analyses, are necessary to detect threats to desired inferences. We propose that educational agencies require and vendors include longitudinal data features, including "match rates" and correlations, as standard exhibits in technical documentation.
ABSTRACT
Since 19 July 2020, Singapore entered Phase 2 of re-opening after one and half month’s “Circuit Breaker” measures to curb the spread of COVID 19. Although most businesses and public places have resumed operation at a reduced capacity, individuals were strongly advised to practice social distancing and avoid crowds. Both implicit and explicit measures to prevent overcrowding had impacted on how people visit places in Singapore. The current study used geotagged Twitter data between September to October in 2020 to examine the spatial and temporal patterns of residents’ locations in Singapore and explored the service amenities which remain “attractive” to residents. Random Forest Supervised Machine Learning Model was used to train and predict spatial distribution of activities during off-work recreational hours using service amenities point of interests (POIs) and land use merge. Five explanatory variables used were parks, public links between parks and malls, taxi stands, residential areas, and shopping malls which had the strongest influence in driving the model prediction of spatial distribution of activities in off-work recreational hours. While distinct temporal patterns of tweets were expected during office hour, this analysis revealed no such statistically significant clusters. The regression analysis showed that distances to service amenities did not provide strong explanations for tweeting patterns. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
In recent years, "Internet + "medical treatment has gradually become an important form of medical care in China. Especially affected by the COVID-19, China′s maternal and child health care institutions have closely integrated "Internet + " technology with maternal health care to meet maternal health needs and reduce maternal risk. Based on a comprehensive analysis of Chinese literature on "Internet + " maternal health care in CNKI, Wanfang database, VIP, CBM in recent five years, this paper introduces the main forms, contents and effects of " Internet + " maternal health care in China, and points out the direction of clinical practice and scientific research in the future, which can be used for reference by the general colleagues. © 2021 Chinese Journal of Practical Nursing. All rights reserved.
ABSTRACT
Five cases of axillary lymphadenopathy are presented, which occurred after COVID-19 vaccination and mimicked metastasis in a vulnerable oncologic patient group. Initial radiologic diagnosis raised concerns for metastasis. However, further investigation revealed that patients received COVID-19 vaccinations in the ipsilateral arm prior to imaging. In two cases, lymph node biopsy results confirmed vaccination-related reactive lymphadenopathy. Ipsilateral axillary swelling or lymphadenopathy was reported based on symptoms and physical examination in COVID-19 vaccine trials. Knowledge of the potential for COVID-19 vaccine-related ipsilateral adenopathy is necessary to avoid unnecessary biopsy and change in therapy. © RSNA, 2021.