人格画像：社交媒体资料在预测个人信息方面有多大信息量？
Personality Profiling: How informative are social media profiles in predicting personal information?
AI总结：
本文比较逻辑回归、朴素贝叶斯、SVM和随机森林在社交媒体数字足迹上预测迈尔斯-布里格斯人格类型的效果，发现SVM准确率最高为20.95%，并提出特征重要性统计框架及类别不平衡处理方法。
摘要：
人格画像已被企业用于定向广告、政治竞选和公共卫生宣传。然而，此类模型的准确性和通用性仍相对未知。本文探讨了人们的在线数字足迹在多大程度上可用于刻画其迈尔斯-布里格斯人格类型。我们分析并比较了四种模型：逻辑回归、朴素贝叶斯、支持向量机（SVM）和随机森林。我们发现，SVM模型在预测完整人格类型方面取得了20.95%的最佳准确率。然而，逻辑回归模型的表现仅略逊一筹，且在训练和预测速度上显著更快。此外，我们开发了一个统计框架，用于评估模型中不同特征集的重要性。我们发现，在直觉/实感（p = 0.032）和思考/情感（p = 0.019）模型中，某些特征比其他特征更具信息量。许多带标签的数据集在社交媒体上的个人特征方面存在严重的类别不平衡问题，我们自己的数据集也不例外。因此，我们强调在报告此类数据集上的模型性能时需要审慎考虑，并比较了多种解决类别不平衡问题的方法。
英文摘要：
Personality profiling has been utilised by companies for targeted advertising, political campaigns and public health campaigns. However, the accuracy and versatility of such models remains relatively unknown. Here we explore the extent to which peoples' online digital footprints can be used to profile their Myers-Briggs personality type. We analyse and compare four models: logistic regression, naive Bayes, support vector machines (SVMs) and random forests. We discover that a SVM model achieves the best accuracy of 20.95% for predicting a complete personality type. However, logistic regression models perform only marginally worse and are significantly faster to train and perform predictions. Moreover, we develop a statistical framework for assessing the importance of different sets of features in our models. We discover some features to be more informative than others in the Intuitive/Sensory (p = 0.032) and Thinking/Feeling (p = 0.019) models. Many labelled datasets present substantial class imbalances of personal characteristics on social media, including our own. We therefore highlight the need for attentive consideration when reporting model performance on such datasets and compare a number of methods to fix class-imbalance problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
