Are You Satisfied with Life?: Predicting Satisfaction with Life from Facebook

Social media can be beneficial in detecting early signs of emotional difficulty. We utilized the Satisfaction with Life (SWL) index as a cognitive health measure and presented models to predict an individuals SWL. Our models considered ego, temporal, and link Facebook features collected through the myPersonality.org project. We demonstrated the strong correlation between Big 5 personality features and SWL, and we used this insight to build two-step Random Forest Regression models from ego features. As an intermediate step, the two-step model predicts Big 5 features that are later incorporated in the SWL prediction models. We showed that the two-step approach more accurately predicted SWL than one-step models. By incorporating temporal features we demonstrated that mood swings do not affect SWL prediction and confirmed SWLs high temporal consistency. Strong link features, such as the SWL of top friends or significant others, increased prediction accuracy. Our final model incorporated ego features, predicted personality features, and the SWL of strong links. The final model out-performed previous research on the same dataset by 45%.

社交媒体可以有益于发现情感困难的早期迹象。我们利用对生活（SWL）指数的满意度作为一种认知健康措施，并提出了模型来预测个人SWL。我们的模型考虑了通过myPersonality.org项目收集的自我，时间和链接Facebook功能。我们证明了5个巨大的5个人格特征与SWL之间的密切相关性，我们使用了这种见解来从自我特征构建两步随机的森林回归模型。作为中间步骤，两步模型预测了SWL预测模型中后来并入的大型5个功能。我们表明，两步方法比一步模型更准确地预测SWL。通过合并时间特征，我们证明情绪波动不会影响SWL预测，并确认了SWL的高时间一致性。强大的链接功能（例如顶级朋友的SWL或其他重要的人）提高了预测准确性。我们的最终模型包含了自我特征，预测的人格特征以及牢固的链接。最终模型的表现超过了对同一数据集的先前研究，提高了45％。