Trust In Online Social Networks Research Articles

An Interaction-Based and Graph-Based Hybrid Approach to Evaluate Trust in Online Social Networks (OSNs)

An Interaction-Based and Graph-Based Hybrid Approach to Evaluate Trust in Online Social Networks (OSNs)

A new model for calculating the maximum trust in Online Social Networks and solving by Artificial Bee Colony algorithm

The social networks are widely used by millions of people worldwide. The trust concept is one of the most important issues in Social Network Analysis (SNA) which highly affects the quantity and quality of the inter-connections, decisions, and interactions among the users in e-commerce or recommendation systems. Many normative algorithms are developed to calculate the trust which most of them are complicated, depend on the network structure, and need lots of critical information that makes them hard to use. The aim of this paper is proposing a descriptive, simple and effective method for calculating the maximal trust and the trust route between any two users of an Online Social Network (OSN). For this purpose, four new models for estimating the trust mechanism of the users are proposed and analyzed using Kolmogorov–Smirnov and Anderson–Darling statistical hypothesis tests to identify and validate the best-fitted model based on 20,613 empirical results gathered from 4552 social network volunteers. Due to the time–complexity of the problem, a meta-heuristic algorithm based on the Artificial Bee Colony (ABC) optimization method is also developed for solving the best-fitted model. The proposed algorithm is simulated in Matlab® over six larger test cases adopted from the Facebook dataset. In order to evaluate the performance of the developed algorithm, the Ant Colony Optimization (ACO) and Genetic Algorithm (GA) based meta-heuristics are also simulated on the same test cases. The comparison of the computational results shows that the ABC approach performs better than the ACO and GA as the size of the network increases.

A Survey on Trust Prediction in Online Social Networks

Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

Personalized content recommendation scheme based on trust in online social networks

SummaryAs various social network services have developed, users are creating and sharing a large amount of content. Concurrently, there have been many studies on recommendation schemes for providing users with content that matches their preferences. In this paper, we propose a trust‐based personalized content recommendation scheme using collaborative filtering in online social network services. The user trust is calculated by analyzing social activities, content usages, and social relationships. In addition, the content trust is calculated by analyzing user expertise and reputations. Collaborative filtering is performed on users who are filtered through the user trust, and recommendation priorities are determined according to the content trust. The proposed scheme can improve the performance of collaborative filtering by eliminating untrustworthy users using user trust. It also improves the accuracy of recommendations since it provides recommendations based on content trust. Therefore, the proposed scheme can improve the performance of recommendation services using collaborative filtering in online social network services that share multimedia content. Performance evaluation is performed in terms of MAE and RMSE, which assesses errors in recommended results to demonstrate the superiority of the proposed scheme. Performance evaluations have shown that errors in the proposed scheme are reduced compared to the existing schemes, improving the accuracy of recommendations.

Trust Assessment in Online Social Networks

Assessing trust in online social networks (OSNs) is critical for many applications such as online marketing and network security. It is a challenging problem, however, due to the difficulties of handling complex social network topologies and conducting accurate assessment in these topologies. To address these challenges, we model trust by proposing the three-valued subjective logic (3VSL) model. 3VSL properly models the uncertainties that exist in trust, thus is able to compute trust in arbitrary graphs. We theoretically prove the capability of 3VSL based on the Dirichlet-Categorical (DC) distribution and its correctness in arbitrary OSN topologies. Based on the 3VSL model, we further design the AssessTrust (AT) algorithm to accurately compute the trust between any two users connected in an OSN. We validate 3VSL against two real-world OSN datasets: Advogato and Pretty Good Privacy (PGP). Experimental results indicate that 3VSL can accurately model the trust between any pair of indirectly connected users in the Advogato and PGP.

Trust propagation algorithm based on learning automata for inferring local trust in online social networks

Online social networks have provided an appropriate infrastructure for users to interact with one another and share information. Since trust is one of the most important factors in forming social interactions, it is necessary in these networks to evaluate trust from one user to another indirectly connected user, using propagating trust along reliable trust paths between the two users. The quality of trust inference based on trust propagation is affected by the length of trust paths and also different aggregation strategies for combining trust values derived from multiple paths. While evaluating trust value based on all paths provides more accurate trust inference results, it is very time consuming to be acceptable in large social networks. Therefore, discovering reliable trust paths is always challenging in these networks. Another important challenge is how to aggregate trust values of multiple paths. In this paper, we first propose a new aggregation strategy on the basis of the standard collaborative filtering. We then present a heuristic algorithm based on learning automata, called DLATrust, for discovering reliable paths between two users and inferring the value of trust using the proposed aggregation strategy. The experimental results conducted on the online social network dataset of Advogato demonstrate that DLATrust can efficiently identify reliable trust paths and predict trust with a high accuracy.

Social purchasing and the influence of social networking: a conceptual view

Internet has enabled businesses to offer their merchandise through web-based applications, of which recent phenomenon includes online social networks (OSNs). This paper studies the influence of OSNs through the lens of perceived trust (PT), social norm (SN), user satisfaction (US) and perceived behavioral control (PBC) to find out how these influence participants of OSNs continuance buying intention. A model of IS continuance intention of web-based application was developed to test the above factors. The results show that trust in OSN is based mainly on the degree of the social relations that users have with their vendors, because they are members on the network, on top of their experiences of web service use. US was influenced by PBC, while US also influenced SN and PT with PT exhibiting a strong relationship with SN. Keywords: continuance intention, OSN, participants, purchasing. JEL Classification: Z13, G21, M10, M31, D11, D12

An enhanced trust prediction strategy for online social networks using probabilistic reputation features

Online Social networks have gained much prominence in the recent years such that it has become an unavoidable means of daily communication. The element of trust in social networks has been studied ever since the inception of online social networks. Trust in online social networks is extremely fragile in nature due to the virtual connections between users in the network. The level of trustworthiness of each user in a social network varies and is usually computed using reputation level of the users. This paper focuses on identifying the features that determine the trust of a user in online social networks using benchmark datasets. We propose a new probabilistic reputation feature model that is better than the raw reputation features. The enhanced trust prediction framework has been tested and validated on three benchmark datasets namely Wikipedia election dataset, Epinions dataset and Slashdot dataset. The proposed probabilistic feature enhances the overall accuracy, F1 score, and area under the ROC for the classifier results significantly. The results have been compared with other state of the art techniques and are found to be efficient.

Trust Evaluation in Online Social Networks Using Generalized Network Flow

In online social networks (OSNs), to evaluate trust from one user to another indirectly connected user, the trust evidence in the trusted paths (i.e., paths built through intermediate trustful users) should be carefully treated. Some paths may overlap with each other, leading to a unique challenge of path dependence , i.e., how to aggregate the trust values of multiple dependent trusted paths. OSNs bear the characteristic of high clustering, which makes the path dependence phenomenon common. Another challenge is trust decay through propagation, i.e., how to propagate trust along a trusted path, considering the possible decay in each node. We analyze the similarity between trust propagation and network flow, and convert a trust evaluation task with path dependence and trust decay into a generalized network flow problem. We propose a modified flow-based trust evaluation scheme GFTrust , in which we address path dependence using network flow, and model trust decay with the leakage associated with each node. Experimental results, with the real social network data sets of Epinions and Advogato, demonstrate that GFTrust can predict trust in OSNs with a high accuracy, and verify its preferable properties.

Toward trustworthy vehicular social networks

Wireless vehicular networks offer the promise of connectivity to vehicles that could provide a myriad of safety and driving-enhancing services to drivers and passengers. With wireless technology available in each car, it is expected that huge amounts of information will be exchanged between vehicles or between vehicles and roadside infrastructure. Due to defective sensors, software viruses, or even malicious intent, legitimate vehicles might inject untrustworthy information into the network. Besides relying on the public key infrastructure, this article proposes a social network approach to study trustworthy information sharing in a vehicular network. We first cover recent research progress in measuring direct trust and modeling indirect trust in online social networks, and then discuss how to apply them to vehicular social networks despite several pressing research challenges.

Socio-psychological factors influencing continuance intention of participants using online social networks to buy

The purpose of this study is to investigate the influence of perceived trust (PT), social norm (SN), user satisfaction (US) and perceived behavioural control (PBC) from the perspective of online social networks (OSNs) and how these factors influence continuance intention of OSN particpants who have ever been influenced to buy on this platform to continue buying from OSNs. Online survey was used to collect data from people who have ever used OSNs to buy, at least once. The WarpPLS 4.0 was used to analyse measurement and structural models resulting in significant evidence in support of PT, SN and US as predictors of OSN continuance intention, different from the traditional web-based transactions. For instance, trust in OSN is revealled to be based mainly on the degree of the social relations that users have with their vendors because they are members on the network, on top of their experiences of Web service use. US were influenced by PBC, while US also influenced SN and PT with PT exhibiting a strong relationship with SN. These results have practical implications for individuals desiring to engage in commercial activities on OSNs.

Security and Trust in Online Social Networks

The enormous success and diffusion that online social networks (OSNs) are encountering nowadays is vastly apparent. Users' social interactions now occur using online social media as communication chan

Trust in online social networks: A multifaceted perspective

In recent years, online social networks (OSNs) have gained great popularity and are now among the most frequently visited sites on the Web. Although security standards and practices are an increasing focus of attention, participants still reveal great amounts of sensitive information in the Web 2.0 environment. Obviously, online social networking takes place in a context of trust. However, trust is a concept with many facets and dimensions. To facilitate trust research in OSNs this article aims at clarifying the role of trust and the relevance of facets of trust, social capital and embeddedness in OSNs. First, the focus is on the individual's decision to trust and on processes through which trust actually emerges. Subsequently, trust is viewed as a structurally embedded asset or a property of relationships and networks that helps to shape interaction patterns within OSNs. A conceptual framework is developed that integrates theoretical concepts from the trust literature, social network and social capital theory, and helps to map different trust-related issues in OSNs.

Current Trust Inference Mechanisms in Web Based Social Networks

Problem statement: Nowadays, web based social networks have gained a lot of attention. Over one billion users participate, communicate and share information on these networks based on trust. Approach: One important issue on online social networks is how much one person can trust another person in the network whom they are not directly connected. Scholars around the world have introduced a variety of models and algorithms for inferring trust in online social networks. This study takes a deep look at current methods, their advantages and disadvantages. Results: All current methods have weaknesses and cannot be considered the best solution for inferring trust. Conclusion: Considering the fact that web based social networks are based on trust upon participants, a more accurate and fast algorithm is needed to infer trustworthiness and help users gain valuable information.

A group trust metric for identifying people of trust in online social networks

With the growth of social network services, the need for identifying trustworthy people has become a primary concern in order to protect users’ vast amounts of information from being misused by unreliable users. In this study, we propose the extended Advogato trust metric that facilitates the identification of trustworthy users associated with each individual user. By incorporating the strength of social relationships, we recursively diffuse a capacity of a target user throughout his/her personal network. Based on the capacity propagation, this paper also presents the capacity-first maximum flow method capable of finding the strongest path pertinent to discovering an ordered set of reliable users and preventing unreliable users from accessing personal networks. Experimental results demonstrate that our approach has advantages over existing representative methods in terms of both the discovery of reliable users and the preventability of unreliable users.

A Survey on the Relationship between Trust and Interest Similarity in Online Social Networks

A remarkable growth in quantity and popularity of online social networks has been observed in recent years. There is a good number of online social networks exists which have over 100 million registered users. Many of these popular social networks offer automated recommendations to their users. This automated recommendations are normally generated using collaborative filtering systems based on the past ratings or opinions of the similar users. Alternatively, trust among the users in the network also can be used to find the neighbors while making recommendations. To obtain the optimum result, there must be a positive correlation exists between trust and interest similarity. Though the positive relations between trust and interest similarity are assumed and adopted by many researchers; no survey work on real life people's opinion to support this hypothesis is found. In this paper, we have reviewed the state-of-the-art research work on trust in online social networks and have presented the result of the survey on the relationship between trust and interest similarity. Our result supports the assumed hypothesis of positive relationship between the trust and interest similarity of the users.

Web 2.0 Social Networks: The Role of Trust

Online social networks (OSNs) have gained enormous popularity in recent years. Hundreds of millions of social network users reveal great amounts of personal information in the Web 2.0 environment that is largely devoid of security standards and practices. The central question in this article is why so many social network users are being so trusting. The focus is on theory-building on trust as a critical issue in OSNs. A theoretical framework is developed, which facilitates a multi-level and multi-dimensional analysis of research problems related to trust in OSNs. First, the structural and relational underpinnings of trust in OSNs are investigated from a governance perspective that integrates concepts of social network theory, social capital and the role of value in relational exchanges. Subsequently, the focus moves to the individual’s decision to trust and to processes through which trust actually emerges. Different types and sources of trust from the trust literature and their importance for trust-related decisions and behaviours in OSNs are discussed. Several research propositions are presented, which contribute to a better understanding of the role of trust and the relevance of facets of trust and social capital in OSNs.