论文题目：Extroverts tweet differently from introverts in Weibo

期刊：

作者：Zhenkun Zhou et al.

发表时间：2018/7/3

数字识别码：10.1140/epjds/s13688-018-0146-8

原文链接：

EPJ Data Science发布的研究Extroverts tweet differently from introverts in Weibo展示了一种通过构建机器学习模型用于自动识别大量个体人群性格的方法。

性格作为驱使人类行为的主导因素，是用来预测个人线上行为和线下行为的绝佳指标。然而，由于问卷调查花费巨大，且存在不可避免的主观性因素，所以很难通过这种传统方法来探究人类性格与其行为之间的联系，也难以在大量人群环境下对调查对象进行深入了解。在日常交流中，线上社交媒体发挥着日益重要的作用，因此，来自北京航空航天大学的Jichang Zhao及其团队认为可以通过大量个体人群的线上足迹推断个体性格，比如通过微博上的推文，并进一步理解其在塑造个体线上行为中的作用。

他们通过收集微博上293名活跃用户的个人性格手机版及其网上个人资料，搭建了一个机器学习模型，成功识别了7000多名用户的性格类型，并将这些用户的性格分为外向型和内向型两大类别。通过在时空模式、在线活动、情感表达和对虚拟荣誉的态度等方面进行系统的比较，他们发现性格外向的人在微博上的行为与性格内向的人存在明显差异。该研究为采用机器学习客观地研究大量个体人群的性格提供了有力证据，同时也揭示出在线资料在探究性格与行为的联系方面的重要作用。

摘要：

As dominant factors driving human actions, personalities can be excellent indicators to predict the offline and online behavior of 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 to gain insight in the context of a large number of individuals. Considering the increasingly important role of online social media in daily communications, we argue that the footprints of massive numbers of individuals, such as tweets on Weibo, can be used as a proxy to infer personality and further understand its function in shaping online human behavior. In this study, a map from self-reports of personalities to online profiles of 293 active users on Weibo is established to train a competent machine learning model, which then successfully identifies more than 7000 users as extroverts or introverts. Systematic comparison from the perspectives of tempo-spatial patterns, online activities, emotional expressions and attitudes to virtual honors show that extroverts indeed behave differently from introverts on Weibo. Our findings provide solid evidence to justify the methodology of employing machine learning to objectively study the personalities of a massive number of individuals and shed light on applications of probing personalities and corresponding behaviors solely through online profiles.

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期刊介绍： covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.

2016 Journal Metrics

Citation Impact

2.982 - 2-year Impact Factor

3.042 - 5-year Impact Factor

1.361 - Source Normalized Impact per Paper (SNIP)

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