Insights about media awareness are by-products of reasoning about online social networks (SNs) focusing on brands or target communities, helping in strategy decision-making on publishing business advertisement and preventing rumor and the spread of illegal information. The key issue we will present is the recently created methodology for finding insights using SNs. The process involves collecting all kinds of unstructured data: from brands to weird life styles, political position to nonsense questions, and combining everything based on a specific subject. Target communities are explored using member's information and their relationships with other communities. Therefore, a distinct model for classification allows the creation of rankings and relationships between products, lifestyles and members. Insights can be generated through rankings and bi- or tri-dimensional association maps, using correspondence analysis and visualization methods. In addition, many demographic variables collected from user profile or even generated by an association expert system allows different cross combinations and filtering. These relationships eliminate bias introduced in a conventional focus groups and/or Internet. Real cases will be presented, demonstrating the potential of this new insight generation tool compared to traditional qualitative analysis.