A significant vulnerability in social sensing based services is false notifications from sensing agents, thereby resulting in inaccurate published information that induces loss of revenue and business goodwill. Existing popular schemes utilize rating feedbacks (over the published information) to quantify the perceived usefulness (quality) of the information. However, these schemes do not reward the confidence of the feedback community and lacks provision to regulate the impact of uncertain feedbacks (ratings), and hence can be easily manipulated. In this paper, we propose a model, called W2Q, to mathematically evaluate the Quality of Information (QoI) as a function of the proportion of positive ratings, total number of ratings, and amortized proportion of uncertain ratings. The proposed model exploits Bayesian inference, and a dual weighted regression model to compute the QoI of any published information. We evaluate the proposed model through an experimental study assuming a crowd sourced-urban application as a proof of concept. Experimental results show that compared with the state-of-the-art Josang's belief model, the resultant QoI score is less susceptible to rogue ratings and captures subtle differences between true and false information.