Trustshop: Building a social trust network

Undergraduate thesis submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Computer Science, May 2022Online social networks have increased in recent years. In many areas, such as businesses and governmental organizations, organizations of all sizes have been using social networks to thrive. As a result of this, the concept of trust comes into the picture, as individuals divulge a large amount of personal information on social networks. It is more important than ever to create and measure trust in a social network for people's privacy, safety, and livelihood. This paper aims to identify ways to use the social network structure and the trust links between them to predict how much two people who are not directly connected could trust one another. Inferring such trust values is hard to compute when person A and person B are unconnected in the social network. This paper presents a graph-based model to evaluate trust in an online social network. It focused on computing the quality assessment of person A for person B, considering that person A and B are unconnected in a given graph of quality assessment of people. The proposed model's accuracy in predicting a trust value is calculated and compared to the multiplicative strategies for trust propagation [11]. This paper also proves how this model can be used in real scenarios to answer how trustworthy someone is innate.Ashesi Universit

The Role of Social Capital in Social Solidarity Initiatives During the Covid-19 Pandemic in Egypt

This qualitative study aimed at studying internal social cohesion in the Egyptian society during Covid-19 pandemic. In our research the notion of social capital acted as a measure of community resilience that was present in the Egyptian social solidarity scene during the peak of the Covid-19 pandemic. The focus is on voluntary associations that mobilize resources to help those in need, often relying on social media for outreach. The main research question is thus: “To what extent did social solidarity online networks in Egypt harvest social capital during the Covid-19 pandemic?”. Key informant interviews discovered that the services provided during Covid-19 pandemic ranged from Personal Protective Equipment to front-line medical personnel to helping those who became unemployed. Study findings showed that social capital has played a major role in establishing social resources, such as trust, norms and values that mobilized networks into action. The strengthening of social capital has also been characterized by strong online presence within the domain of social media platforms that empowered these unofficial networks to act as an informal communication and information channel in times of crisis and uncertainty of the pandemic. Further research would suggest the need for a more in-depth assessment of social capital in the Egyptian context, as well as devising further tools on how to formalize these networks in order to optimize social capital in times of crisis

Understanding the Roles of Knowledge Sharing and Trust in Online Learning Communities

This paper builds on action and design research aimed at enhancing scholarly community and conversation in a graduate school setting. In this paper we focus on knowledge sharing (KS) and trust as important factors for building a sustainable online learning community (OLC). Guided by theories of social learning and social networking, we survey graduate students to assess their perceptions of KS and trust in communities of practice (CoPs). These results are compared against posttest results measuring community building and knowledge sharing in a stakeholder-defined OLC. Results indicate that although students ’ initial assessment of KS and trust in CoPs were low, users perceived high levels of value from a stakeholder-defined OLC. Our research offers a proof-of-concept that stakeholder-defined OLCs provide students with the opportunity to develop knowledge networks, while also providing for individual autonomy over their content. Our results also indicate an intriguing alternative to traditional course management systems (CMS)

Collaborative assessment of information provider's reliability and expertise using subjective logic

Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes (reliability and expertise) significantly affect the quality of the answer/information provided. We present a novel approach for estimating these user's characteristics relying on human cognitive traits. In brief, we propose each user to monitor the activity of her peers (on the basis of responses to questions asked by her) and observe their compliance with predefined cognitive models. These observations lead to local assessments that can be further fused to obtain a reliability and expertise consensus for every other user in the social network (SN). For the aggregation part we use subjective logic. To the best of our knowledge this is the first study of this kind in the context of Q&A SN. Our proposed approach is highly distributed; each user can individually estimate the expertise and the reliability of her peers using her direct interactions with them and our framework. The online SN (OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assessment in order to reach a consensus. We emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter's behavior change, provided that the cognitive traits hold in practice. © 2011 ICST

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas

A Cognitive-based scheme for user reliability and expertise assessment in Q&A social networks

Q&A social media has gained a great deal of attention during recent years. People rely on these sites to obtain information due to the number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradictory answers, causing ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. In this work, we propose a novel approach for estimating the reliability and expertise of a user based on human cognitive traits. Every user can individually estimate these values based on local pairwise interactions. We examine the convergence performance of our algorithm and we find that it can accurately assess the reliability and the expertise of a user and can successfully react to the latter's behavior change. © 2011 IEEE

The case of online trust

“The original publication is available at www.springerlink.com”. Copyright SpringerThis paper contributes to the debate on online trust addressing the problem of whether an online environment satisfies the necessary conditions for the emergence of trust. The paper defends the thesis that online environments can foster trust, and it does so in three steps. Firstly, the arguments proposed by the detractors of online trust are presented and analysed. Secondly, it is argued that trust can emerge in uncertain and risky environments and that it is possible to trust online identities when they are diachronic and sufficient data are available to assess their reputation. Finally, a definition of trust as a second-order property of first-order relation is endorsed in order to present a new definition of online trust. According to such a definition, online trust is an occurrence of trust that specifically qualifies the relation of communication ongoing among individuals in digital environments. On the basis of this analysis, the paper concludes by arguing that online trust promotes the emergence of social behaviours rewarding honest and transparent communications.Peer reviewe