Personality profiling has been utilised by companies for targeted
advertising, political campaigns and vaccine campaigns. However, the accuracy
and versatility of such models still remains relatively unknown. Consequently,
we aim to explore the extent to which peoples' online digital footprints can be
used to profile their Myers-Briggs personality type. We analyse and compare the
results of 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 someones complete personality type.
However, logistic regression models perform only marginally worse and are
significantly faster to train and perform predictions. We discover that many
labelled datasets present substantial class imbalances of personal
characteristics on social media, including our own. As a result, we highlight
the need for attentive consideration when reporting model performance on these
datasets and compare a number of methods for fixing the class-imbalance
problems. 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. While we apply these methods to
Myers-Briggs personality profiling, they could be more generally used for any
labelling of individuals on social media.

通过分析个人的在线数字足迹，我们比较和研究了逻辑回归、朴素贝叶斯、支持向量机和随机森林等四个模型在预测人们完整性格类型方面的准确性和多样性，结果显示 SVM 模型以 20.95% 的准确性表现最优，尽管逻辑回归模型稍有劣势，但培训速度和预测性能明显更快，我们发现社交媒体上的个人特征标记数据集以及其他数据集都存在较大的类别不平衡问题，因此强调了在这些数据集上报告模型性能时需予以谨慎，同时我们比较了几种解决类别不平衡问题的方法，并开发了一个统计框架来评估模型中不同特征集的重要性，研究发现，对于直觉 / 感知模型（p = 0.032）和思维 / 情感模型（p = 0.019），某些特征相对于其他特征更具信息量，尽管我们将这些方法应用于 Myers-Briggs 性格分类中，但它们也可以更广泛地用于社交媒体上对个体的标记。

人格分析：社交媒体个人资料对预测个人信息有多大的信息量？

Personality Profiling: How informative are social media profiles in  predicting personal information?

Personality types are important in various fields as they hold relevant
information about the characteristics of a human being in an explainable
format. They are often good predictors of a person's behaviors in a particular
environment and have applications ranging from candidate selection to marketing
and mental health. Recently automatic detection of personality traits from
texts has gained significant attention in computational linguistics. Most
personality detection and analysis methods have focused on small datasets
making their experimental observations often limited. To bridge this gap, we
focus on collecting and releasing the largest automatically curated dataset for
the research community which has 152 million tweets and 56 thousand data points
for the Myers-Briggs personality type (MBTI) prediction task. We perform a
series of extensive qualitative and quantitative studies on our dataset to
analyze the data patterns in a better way and infer conclusions. We show how
our intriguing analysis results often follow natural intuition. We also perform
a series of ablation studies to show how the baselines perform for our dataset.

自动检测个性特征的研究方法已引起计算语言学领域的广泛关注，本文针对此领域中数据集有限的问题，收集和发布了一个包含 1.52 亿推文和 5.6 万份数据点的研究社区最大的自动整理数据集，以用于预测迈尔斯 - 布里格斯（MBTI）人格类型，通过对数据进行定性和定量研究，分析了数据的模式，并得出了相应的结论。