This paper estimates new confidence bounds on source reliability in social sensing applications. Scalable and robust estimation of source reliability is a key challenge in social sensing where humans or human-operated sensors act as data sources. In order to assess correctness of data, the reliability of sources must first be assessed, yet this is complicated when sources are not a priori known and vetted, but rather can opt in at will, for example, by downloading a sensing application on their mobile device. In our previous work, we developed a maximum likelihood source reliability estimator and approximately quantified confidence in its estimation based on an asymptotic Cramer-Rao lower bound (CRLB). In this paper we show that the asymptotic bound fails to track estimation performance when the number of sources is small. We derive the real CRLB to accurately characterize estimation performance for scenarios where the asymptotic bound fails. We study the limitations of the real and asymptotic CRLBs and show the trade-offs they offer between computational complexity and estimation scalability. We also evaluate the robustness of these bounds to changes in the number of sources. The results offer an understanding of attainable estimation accuracy of source reliability in social sensing applications that rely on un-vetted sources whose reliability is not known in advance.