Drop computing is a network paradigm that aims to address the issues of the mobile cloud computing model, which has started to show limitations especially since the advent of the Internet of Things and the increase in the number of connected devices. In drop computing, nodes are able to offload data and computations to the cloud, to edge devices, or to the social-based opportunistic network composed of other nodes located nearby. In this paper, we focus on the lowest layer of drop computing, where mobile nodes offload tasks and data to and from each other through close-range protocols, based on their social connections. In such a scenario, where the data can circulate in the mobile network on multiple paths (and through multiple other devices), consistency issues may appear due to data corruption or malicious intent. Since there is no central entity that can control the way information is spread and its correctness, alternative methods need to be employed. In this paper, we propose several mechanisms for ensuring data consistency in drop computing, ranging from a rating system to careful analysis of the data received. Through thorough experimentation, we show that our proposed solution is able to maximize the amount of correct (i.e., uncorrupted) data exchanged in the network, with percentages as high as 100%.