Memes are topics that spread among people in a community. They could be ideas, behaviors, or styles, etc. Recent research found that the spreading of memes on microblogs displays epidemic patterns, while their popularity is predictable. Aiming for high accuracy, complex models that consider the structural details of community graphs are common. On the contrary, some models simply assume a community is evenly connected to sacrifice accuracy for efficiency. In this paper, we propose a prediction service that delivers the benefits of both worlds. Our service considers the properties of community graphs yet without the burden of their structural details to reduce complexity for online consumption. By comparing the viralities of memes against a community threshold, memes that would lead to population-wide coverage are identified. We also carefully examine sampling biases. Hence, reliable thresholds and accurate predictions are made as shown in our experiments.