Contagious diseases pose significant challenges to public healthcare systems all over the world. The rise in emerging contagious and infectious diseases has led to calls for the use of new techniques and technologies capable of detecting, tracking, mapping and managing behavioral patterns in such diseases. In this study, we used Big Data technologies to analyze two sets of flu (influenza) activity data: Twitter data were used to extract behavioral patterns from a location-based social network and to monitor flu outbreaks (and their locations) in the US, and Cerner HealthFacts data warehouse was used to track real-world clinical encounters. We expected that the integration (mashing) of social media and real-world clinical encounters could be a valuable enhancement to the existing surveillance systems. Our results verified that flu-related traffic on social media is closely related with actual flu outbreaks. However, rather than using simple Pearson correlation, which assumes a zero lag between the online and real-world activities, we used a multi-method data analytics approach to obtain the spatio-temporal cross-correlation between the two flu trends and to explain behavioral patterns during the flu season. We found that clinical flu encounters lag behind online posts. Also, we identified several public locations from which a majority of posts initiated. These findings can help health authorities develop more effective interventions (behavioral and/or otherwise) during the outbreaks to reduce the spread and impact, and to inform individuals about the locations they should avoid during those periods.