  With the booming development and popularity of mobile applications, different
verticals accumulate abundant data of user information and social behavior,
which are spontaneous, genuine and diversified. However, each platform
describes user's portraits in only certain aspect, resulting in difficult
combination of those internet footprints together. In our research, we proposed
a modeling approach to analyze user's online behavior across different social
media platforms. Structured and unstructured data of same users shared by
NetEase Music and Sina Weibo have been collected for cross-platform analysis of
correlations between music preference and other users' characteristics. Based
on music tags of genre and mood, genre cluster of five groups and mood cluster
of four groups have been formed by computing their collected song lists with
K-means method. Moreover, with the help of user data of Weibo, correlations
between music preference (i.e. genre, mood) and Big Five personalities (BFPs)
and basic information (e.g. gender, resident region, tags) have been
comprehensively studied, building up full-scale user portraits with finer
grain. Our findings indicate that people's music preference could be linked
with their real social activities. For instance, people living in mountainous
areas generally prefer folk music, while those in urban areas like pop music
more. Interestingly, dog lovers could love sad music more than cat lovers.
Moreover, our proposed cross-platform modeling approach could be adapted to
other verticals, providing an online automatic way for profiling users in a
more precise and comprehensive way.
