Extroverts Tweet Differently from Introverts in Weibo (1703.06637v1)

Abstract: 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.