Civic engagement and corruption in 20 European democracies

This paper analyzes the relation between different forms of civic engagement and corruption. This first of all extends earlier analysis linking generalized trust to corruption by incorporating another element from the social capital complex (namely formal forms of civic engagement). Second, based on the idea that social networks' beneficial or harmful impact may depend on their characteristics, it investigates how the structure of social networks (i.e., inclusive vs. exclusive and isolated vs. connected) matters. Evaluating the engagement - corruption nexus for a cross-section of 20 European democracies in 2002/2003, we confirm that social networks are linked to corruption even when controlling for the effect of generalized trust, and that their relation to corruption is typespecific. These findings survive under various model specifications and robustness checks. --Corruption,civil society,networks,voluntary associations,European social survey

A Text-based Model for Identifying Online Trust Relationships

Trust has been a long-standing issue in online communities and is gaining importance with the popularity of online social networks. Traditional trust models theorize and explain trust, but they do not directly provide operationalization of trust relationships. In view that the text is the dominant medium of online communication, this paper develops a text-based model for identifying online trust relationships. Building on organizational trust models, social exchange theory, and speech-act theory, the proposed model conceptualizes trust relationship as a sequence of speech acts. The model is validated with the data collected from a real-world online community. This research not only creates a text-based method for identifying online trust but also lays the groundwork for automated analysis of online trust

MODELING THE PROTECTION OF PERSONAL DATA FROM TRUST AND THE AMOUNT OF INFORMATION ON SOCIAL NETWORKS

The article analyzes the parameters of social networks. The analysis is performed to identify critical threats. Threats may lead to leakage or damage to personal data. The complexity of this issue lies in the ever-increasing volume of data. Analysts note that the main causes of incidents in Internet resources are related to the action of the human factor, the mass hacking of IoT devices and cloud services. This problem is especially exacerbated by the strengthening of the digital humanistic nature of education, the growing role of social networks in human life in general. Therefore, the issue of personal information protection is constantly growing. To address this issue, let’s propose a method of assessing the dependence of personal data protection on the amount of information in the system and trust in social networks. The method is based on a mathematical model to determine the protection of personal data from trust in social networks. Based on the results of the proposed model, modeling was performed for different types of changes in confidence parameters and the amount of information in the system. As a result of mathematical modeling in the MatLab environment, graphical materials were obtained, which showed that the protection of personal data increases with increasing factors of trust in information. The dependence of personal data protection on trust is proportional to other data protection parameters. The protection of personal data is growing from growing factors of trust in information. Mathematical modeling of the proposed models of dependence of personal data protection on trust confirmed the reliability of the developed model and proved that the protection of personal data is proportional to reliability and trus

Credibility-based social network recommendation: Follow the leader

In Web-based social networks (WBSN), social trust relationships between users indicate the similarity of their needs and opinions. Trust can be used to make recommendations on the web because trust information enables the clustering of users based on their credibility which is an aggregation of expertise and trustworthiness. In this paper, we propose a new approach to making recommendations based on leaders' credibility in the "Follow the Leader" model as Top-N recommenders by incorporating social network information into user-based collaborative filtering. To demonstrate the feasibility and effectiveness of "Follow the Leader" as a new approach to making recommendations, first we develop a new analytical tool, Social Network Analysis Studio (SNAS), that captures real data and used it to verify the proposed model using the Epinions dataset. The empirical results demonstrate that our approach is a significantly innovative approach to making effective collaborative filtering based recommendations especially for cold start users. © 2010 Al-Sharawneh & Williams

A Computational Model to Evaluate Honesty in Social Internet of Things

Trust in Social Internet of Things has allowed to open new horizons in collaborative networking, particularly by allowing objects to communicate with their service providers, based on their relationships analogy to human world. However, strengthening trust is a challenging task as it involves identifying several influential factors in each domain of social-cyber-physical systems in order to build a reliable system. In this paper, we address the issue of understanding and evaluating honesty that is an important trust metric in trustworthiness evaluation process in social networks. First, we identify and define several trust attributes, which affect directly to the honesty. Then, a subjective computational model is derived based on experiences of objects and opinions from friendly objects with respect to identified attributes. Based on the outputs of this model a final honest level is predicted using regression analysis. Finally, the effectiveness of our model is tested using simulations