scholarly journals What Accounts for the Variation in COVID-19 Vaccine Hesitancy in Eastern, Southern and Western Europe?

2022 ◽  
Author(s):  
Dimiter Toshkov

Attitudes towards vaccination have proven to be a major factor determining the pace of national COVID-19 vaccination campaigns throughout 2021. In Europe, large differences in levels of vaccine hesitancy and refusal have emerged, which are highly correlated with actual vaccination levels. This article explores attitudes towards COVID-19 vaccination in 27 European countries based on data from Eurobarometer (May 2021). The statistical analyses show that demographic variables have complex effects on vaccine hesitancy and refusal. Trust in different sources of health-related information has significant effects as well, with people who trust the Internet, social networks and ‘people around’ in particular being much more likely to express vaccine skepticism. As expected, beliefs in the safety and effectiveness of vaccines have large predictive power, but – more interestingly – net of these two beliefs, the effects of trust in Internet, online social networks and people as sources of health information are significantly reduced. This study shows that the effects of demographic, belief-related and other individual-level factors on vaccine hesitancy and refusal are context-specific. Yet, explanations of the differences in vaccine hesitancy across Europe need to consider primarily different levels of trust and vaccine-relevant beliefs, and to a lesser extent their differential effects.

Author(s):  
Shruti Kohli ◽  
Sonia Saini

Recent work in machine learning and natural language processing has studied the content of health related information in tweets and demonstrated the potential for extracting useful public health information from their aggregation. Social intelligence derived from health content has become of significant importance for various applications, including post-marketing drug surveillance, competitive intelligence, medicine reviews and to assess health-related opinions and sentiments. Further, the quantity of medical information in the media such as tweets on Twitter, Facebook or medical blogs is growing at an exponential rate. Medical data such as health records, drug data, etc. has become major candidates for Big Data analysis and thus exploring this content has become a necessity for organizations. However, the volume, velocity, variety, and quality of online health information present challenges, necessitating enhanced facilitation mechanisms for medical social computing. The objective of this chapter is to discuss the possibility of mining medical trends using Social Networks.


Author(s):  
Elvira Ortiz-Sánchez ◽  
Almudena Velando-Soriano ◽  
Laura Pradas-Hernández ◽  
Keyla Vargas-Román ◽  
Jose L. Gómez-Urquiza ◽  
...  

The aim of this study was to analyze social networks’ information about the anti-vaccine movement. A systematic review was performed in PubMed, Scopus, CINAHL and CUIDEN databases. The search equations were: “vaccine AND social network” and “vaccine AND (Facebook[title] OR Twitter[title] OR Instagram[title] OR YouTube[title])”. The final sample was n = 12, including only articles published in the last 10 years, in English or Spanish. Social networks are used by the anti-vaccine groups to disseminate their information. To do this, these groups use different methods, including bots and trolls that generate anti-vaccination messages and spread quickly. In addition, the arguments that they use focus on possible harmful effects and the distrust of pharmaceuticals, promoting the use of social networks as a resource for finding health-related information. The anti-vaccine groups are able to use social networks and their resources to increase their number and do so through controversial arguments, such as the economic benefit of pharmaceuticals or personal stories of children to move the population without using reliable or evidence-based content.


Author(s):  
Janine Hacker ◽  
Nilmini Wickramasinghe ◽  
Carolin Durst

One of the serious concerns in healthcare in this 21st century is obesity. While the causes of obesity are multifaceted, social networks have been identified as one of the most important dimensions of people's social environment that may influence the adoption of many behaviours, including health-promoting behaviours. In this article, we examine the possibility of harnessing the appeal of online social networks to address the obesity epidemic currently plaguing society. Specifically, a design science research methodology is adopted to design, implement and test the Health 2.0 application called “Calorie Cruncher”. The application is designed specifically to explore the influence of online social networks on individual’s health-related behaviour. In this regard, pilot data collected based on qualitative interviews indicate that online social networks may influence health-related behaviours in several ways. Firstly, they can influence people’s norms and value system that have an impact on their health-related behaviours. Secondly, social control and pressure of social connections may also shape health-related behaviours, and operate implicitly when people make food selection decisions. Thirdly, social relationships may provide emotional support. Our study has implications for research and practice. From a theoretical perspective, the article inductively identifies three factors that influence specific types of health outcomes in the context of obesity. From a practical perspective, the study underscores the benefits of adopting a design science methodology to design and implement a technology solution for a healthcare issue as well as the key role for online social media to assist with health and wellness management and maintenance.


Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 119
Author(s):  
Thanh Trinh ◽  
Dingming Wu ◽  
Joshua Zhexue Huang ◽  
Muhammad Azhar

Event-based social networks (EBSNs) are widely used to create online social groups and organize offline events for users. Activeness and loyalty are crucial characteristics of these online social groups in terms of determining the growth or inactiveness of the social groups in a specific time frame. However, there is less research on these concepts to clarify the existence of groups in event-based social networks. In this paper, we study the problem of group activeness and user loyalty to provide a novel insight into online social networks. First, we analyze the structure of EBSNs and generate features from the crawled datasets. Second, we define the concepts of group activeness and user loyalty based on a series of time windows, and propose a method to measure the group activeness. In this proposed method, we first compute a ratio of a number of events between two consecutive time windows. We then develop an association matrix to assign the activeness label for each group after several consecutive time windows. Similarly, we measure the user loyalty in terms of attended events gathered in time windows and treat loyalty as a contributive feature of the group activeness. Finally, three well-known machine learning techniques are used to verify the activeness label and to generate features for each group. As a consequence, we also find a small group of features that are highly correlated and result in higher accuracy as compared to the whole features.


Author(s):  
Huan Li ◽  
Kejie Lu ◽  
Qi Zhang

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec2. The main ideas of H-Rec2 include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec2 framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.


2017 ◽  
Vol 4 (2) ◽  
pp. 160863 ◽  
Author(s):  
Mahdi Jalili ◽  
Yasin Orouskhani ◽  
Milad Asgari ◽  
Nazanin Alipourfard ◽  
Matjaž Perc

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.


2015 ◽  
Vol 235 (2) ◽  
pp. 139-167 ◽  
Author(s):  
Artjoms Ivlevs ◽  
Timothy Hinks

Summary We study the individual-level determinants of bribing public officials. Particular attention is paid to the issue of respondents’ non-random selection into contact with public officials, which may result in biased estimates. Data come from the 2010 Life in Transition Survey, covering 30 post-socialist and five Western European countries. The results suggest that the elderly tend to be less likely to bribe public officials, while people with higher income and, especially, low trust in public institutions are more likely to bribe. Several determinants of bribery - ethnic minority status, the degree of urbanisation, social trust - are context specific, i.e. they change signs or are statistically significant according to the geographical region or the type of public official. The results show that not accounting for sample selection effects may produce a bias in estimated coefficients.


Author(s):  
Yousra Asim ◽  
Ahmad Kamran Malik

Online Social Networks (OSN) are getting popular day by day. Users share their information in OSN with others users. Access control is required to prevent unauthorized access to this information. Several studies have been conducted for access control in social networks. This chapter is a survey of available access control models/techniques based on social networks. Available access control models can be categorized as relationship-based, attributes-based, community structure-based and user activity centric model. A number of techniques have been proposed by several authors for access control in social networks. Most of the approaches use Social Network Analysis (SNA) techniques, others use user related information, for example, attributes or activities, the rest use a combination of approaches.


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