  Being dominant factors driving the human actions, personalities can be
excellent indicators in predicting the offline and online behavior of different
individuals. However, because of the great expense and inevitable subjectivity
in questionnaires and surveys, it is challenging for conventional studies to
explore the connection between personality and behavior and gain insights in
the context of large amount individuals. Considering the more and more
important role of the online social media in daily communications, we argue
that the footprint of massive individuals, like tweets in Weibo, can be the
inspiring proxy to infer the personality and further understand its functions
in shaping the online human behavior. In this study, a map from self-reports of
personalities to online profiles of 293 active users in Weibo is established to
train a competent machine learning model, which then successfully identifies
over 7,000 users as extroverts or introverts. Systematical comparisons from
perspectives of tempo-spatial patterns, online activities, emotion expressions
and attitudes to virtual honor surprisingly disclose that the extrovert indeed
behaves differently from the introvert in Weibo. Our findings provide solid
evidence to justify the methodology of employing machine learning to
objectively study personalities of massive individuals and shed lights on
applications of probing personalities and corresponding behaviors solely
through online profiles.
