Advances in the sensing capabilities of smartphones have resulted in the emergence of participatory sensing. In participatory sensing, ordinary citizens are recruited to collect sensor data from nearby environments which are then analysed to provide useful information. The information credibility is predominantly dependent on sufficient participation. There are however, various costs associated with contributing data including time, phone battery and bandwidth consumption and potential exposure to privacy threats. These issues may dissuade participants from contributing, thus decreasing the data quality. The integration of social networks with participatory sensing, referred to as social participatory sensing is a potential solution since it provides access to social network members as participants. This integration however, raises new challenges. First, is the potential sparseness of the requester's friendship graph which affects the ability to recruit sufficient contributors. Second, is the identification of well-suited participants who can fulfil the task's requirements. Third, is assessing the trustworthiness of provided contributions. In this thesis, we propose an innovative framework comprising novel strategies that address the aforementioned issues. We first present a recruitment scheme that addresses the participation sufficiency issue by utilising friendship relations to provide access to adequate participants. The scheme also identifies credible communication paths to preserve the integrity and privacy of messages. Next, we design a participant selection scheme to select well-suited participants from a wider pool. Our scheme also prevents collusion among the selected group. Finally, we present a trust assessment scheme for comprehensive trust evaluation encompassing all personal and social influential parameters. The trust scores are then used to update the participants' reputations. The proposed ideas have been experimentally validated on real-world datasets. Results show that our framework is able to effectively address the participant sufficiency by recruiting the required participants with twice the suitability as that achieved by comparable methods. Moreover, it can accurately detect 83% of possible collusion instances. Our framework is also successful in increasing the overall trust to 90% which is 15% greater than that achieved by compared methods. To sum up, our proposed framework is successful in comprehensively addressing the challenges of social participatory sensing in an application-agnostic manner.