Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. In addition to individuals using such networks to connect to their friends and families, governments and enterprises have started exploiting these platforms for delivering their services to citizens and customers. However, the success of such attempts relies on the level of trust that members have with each other as well as with the service provider. Therefore, trust becomes an essential and important element of a successful social network. In this article, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. We then discuss recent works addressing three aspects of social trust: trust information collection , trust evaluation , and trust dissemination . Finally, we compare and contrast the literature and identify areas for further research in social trust.

In this paper, we propose and develop a platform to support data collection for mobile crowdsensing from mobile device sensors that is under-pinned by real-time mobile data stream mining. We experimentally show that mobile data mining provides an efficient and scalable approach for data collection for mobile crowdsensing. Our approach results in reducing the amount of data sent, as well as the energy usage on the mobile phone, while providing comparable levels of accuracy to traditional models of intermittent/continuous sensing and sending. We have implemented our Context-Aware Real-time Open Mobile Miner (CAROMM) to facilitate data collection from mobile users for crowdsensing applications. CAROMM also collects and correlates this real-time sensory information with social media data from both Twitter and Facebook. CAROMM supports delivering real-time information to mobile users for queries that pertain to specific locations of interest. We have evaluated our framework by collecting real-time data over a period of days from mobile users and experimentally demonstrated that mobile data mining is an effective and efficient strategy for mobile crowdsensing.

We propose a trust model for social networks with the aim of building trust communities that inspire members to share their experiences, feelings and opinions in an open and honest way without the fear of being judged. The unique feature of our model is that the trust value is derived from the social capital built in the social networks over a period of time. First, we introduce a framework for building trust communities using social trust. We then define an underlying social trust model, called STrust. Finally, we report the current state of the development and the analysis of the proposed trust model.

Reputation systems are typically based on ratings given by the users. When there are no mechanisms in place to detect collusion and deception, combining user testimonies as such to form a provider's reputation may not give an accurate assessment, especially if the context of the ratings is not known. Moreover, such systems are vulnerable to manipulations by malicious users. Hence it becomes essential to establish the validity of the ratings prior to using them in formulating reputation based on such ratings. It is important to identify the rationale behind the ratings so that similar ratings (or ratings pertaining to a context) can be aggregated to obtain a reputation value meaningful in that context. We propose a fuzzy approach to analyze user rating behavior to infer the rationale for ratings in a web services environment. This inference of rationale facilitates the system to validate ratings, detect deception and collusion, identify user preferences and provide recommendations to users.

No abstract