With the proliferation of social sensing, large amounts of observation are contributed by people or devices. However, these observations contain disinformation. Disinformation can propagate across online social networks at a relatively low cost, but result in a series of major problems in our society. In this survey, we provide a comprehensive overview of disinformation and truth discovery in social sensing under a unified perspective, including basic concepts and the taxonomy of existing methodologies. Furthermore, we summarize the mechanism of disinformation from four different perspectives (i.e., text only, text with image/multi-modal, text with propagation, and fusion models). In addition, we review existing solutions based on these requirements and compare their pros and cons and give a sort of guide to usage based on a detailed lesson learned. To facilitate future studies in this field, we summarize related publicly accessible real-world data sets and open source codes. Last but the most important, we emphasize potential future research topics and challenges in this domain through a deep analysis of most recent methods.