  Online social media such as the micro-blogging site Twitter has become a rich
source of real-time data on online human behaviors. Here we analyze the
occurrence and co-occurrence frequency of keywords in user posts on Twitter.
From the occurrence rate of major international brand names, we provide
examples on predictions of brand-user behaviors. From the co-occurrence rates,
we further analyze the user-perceived relationships between international brand
names and construct the corresponding relationship networks. In general the
user activity on Twitter is highly intermittent and we show that the occurrence
rate of brand names forms a highly correlated time signal.
