Goal contents pursuit reflects the motivational personality and can be an excellent indicator to predict individuals' life satisfaction and daily behaviors. However, due to the expense and subjective bias of questionnaires, it is challenging to obtain individual data and explore the effects of goal contents pursuit in conventional studies. Social media provides individuals with a communication context that can be used as a proxy to infer personality based on a massive of media footprints information. This study obtained 456 Weibo active users' self-reports of goal contents pursuit scale and their online behaviors that is established to train a competent machine learning model, which then successfully identifies the classification of intrinsic and extrinsic goals. From the perspective of Weibo users' features (i.e., basic, interactive, linguistic, and emotional features), the systematic comparison shows the significant differences in the pursuit level of intrinsic and extrinsic goals. This study advances the methodology of employing machine learning and online data to objectively delineate individual goal contents pursuit and paves the way to explore a massive number of individuals' personalities and behaviors.

中文翻译：

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预测社交媒体上的内在和外在目标内容追求

目标内容追求反映了动机人格，是预测个人生活满意度和日常行为的一个很好的指标。然而，由于问卷的费用和主观偏见，在常规研究中获取个体数据和探索目标内容追求的影响具有挑战性。社交媒体为个人提供了一个交流环境，可以用作代理来根据大量的媒体足迹信息推断个性。本研究获取了456位微博活跃用户对目标内容追求量表及其在线行为的自我报告，建立了一个称职的机器学习模型，进而成功识别了内在和外在目标的分类。从微博用户的特征（即基础性、互动性、语言和情感特征），系统比较表明内在和外在目标的追求水平存在显着差异。本研究推进了利用机器学习和在线数据客观描述个人目标内容追求的方法，为探索大量个人的个性和行为铺平了道路。