Extroverts Tweet Differently from Introverts in Weibo
Zhenkun Zhou, Ke Xu, Jichang Zhao

TL;DR
This study demonstrates that machine learning models can effectively infer personality types, such as extroversion and introversion, from Weibo social media activity, revealing distinct behavioral patterns.
Contribution
The paper introduces a novel approach to classify personalities using online social media data, bypassing traditional subjective questionnaires and surveys.
Findings
Extroverts and introverts exhibit different online behaviors in Weibo.
Machine learning models can accurately identify personality types from social media footprints.
Distinct emotional expressions and activity patterns correlate with personality traits.
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…
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Taxonomy
TopicsOpinion Dynamics and Social Influence · Personality Traits and Psychology · Complex Network Analysis Techniques
