Given the importance of developing accurate conceptual models of the system of health-related factors like obesity, the modeling process often seeks to be comprehensive by including experts and community members. While many qualitative modeling processes can produce models in the form of maps (e.g., cognitive/concept mapping, causal loop diagrams), they are generally conducted with a facilitator. The limited capacity of the facilitators limits the number and geographical diversity of participants. In addition, participants may not openly express their beliefs (e.g., weight discrimination) when perceiving that they may not be well received by a facilitator or others in the room. In contrast, the naturally occurring exchange of perspectives through social media provides an unobtrusive approach to collecting beliefs on the causes and consequences of obesity and it also supports a scalable approach and a geographically diverse sample. While obtaining a model via social media can inform policymakers about popular support for possible policies, the model may stand in stark contrast to an expert-based model. Identifying and reconciling these differences is an important step to integrating social computing with policy making.