This paper presents a demonstration of our SECON 2015 paper using Twitter based case studies for social sensing applications. Social sensing has emerged as a new paradigm of data collection, where a group of individuals volunteer (or are recruited) to share certain observations or measurements about the physical world. A key challenge in social sensing applications lies in ascertaining the correctness of reported observations from unvetted data sources with unknown reliability. We refer to this problem as truth estimation. In this paper, we showed a demo of a new confidence-aware truth estimation scheme that explicitly considers different degrees of confidence that sources express on the reported data. In the demo session: the participants will have a chance to (i) play with the tool on some historic datasets we have collected from Twitter; (ii) send live queries to Twitter and perform real-time truth estimation analysis in the events of their interests.