This paper proposes a method to discover unexpected consumer behavior from buzz marketing sites. For example, in 2009, the super-flu virus spawned significant effects on various product marketing domains around the globe. We could easily expect air purifiers to be sold due to flu pandemic. But, in parallel, we could observe the reluctance of consumers to buy digital single-lens reflex camera triggered by flu pandemic. The reluctance to buy digital single-lens reflex cameras because of the flu is not something we would have expected. This paper applies data mining techniques to analyze this sort of unexpected consumer behavior caused by a current topic like the flu. The unforeseen relationship between a current topic and products is modeled and visualized using a directed graph, and consumer behavior is further analyzed based on the time series variation of directed graph structures.
