機器自識別｜社交媒體上你的性格暴露

論文題目：Extroverts tweet differently from introverts in Weibo

作者：Zhenkun Zhou et al.

數字識別碼：10.1140/epjds/s13688-018-0146-8

EPJ Data Science發布的研究Extroverts tweet differently from introverts in Weibo展示了一種通過構建機器學習模型用於自動識別大量個體人群性格的方法。

性格作為驅使人類行為的主導因素，是用來預測個人線上行為和線下行為的絕佳指標。然而，由於問卷調查花費巨大，且存在不可避免的主觀性因素，所以很難通過這種傳統方法來探究人類性格與其行為之間的聯繫，也難以在大量人群環境下對調查對象進行深入了解。在日常交流中，線上社交媒體發揮著日益重要的作用，因此，來自北京航空航天大學的Jichang Zhao及其團隊認為可以通過大量個體人群的線上足跡推斷個體性格，比如通過微博上的推文，並進一步理解其在塑造個體線上行為中的作用。

他們通過收集微博上293名活躍用戶的個人性格報告及其網上個人資料，搭建了一個機器學習模型，成功識別了7000多名用戶的性格類型，並將這些用戶的性格分為外向型和內向型兩大類別。通過在時空模式、在線活動、情感表達和對虛擬榮譽的態度等方面進行系統的比較，他們發現性格外向的人在微博上的行為與性格內向的人存在明顯差異。該研究為採用機器學習客觀地研究大量個體人群的性格提供了有力證據，同時也揭示出在線資料在探究性格與行為的聯繫方面的重要作用。

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.

期刊介紹：EPJ Data Sciencecovers 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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