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?

We study the problem of user segmentation: given a set of users and one or
more predefined groups or segments, assign users to their corresponding
segments. As an example, for a segment indicating particular interest in a
certain area of sports or entertainment, the task will be to predict whether
each single user will belong to the segment. However, there may exist numerous
long tail prediction tasks that suffer from data availability and may be of
heterogeneous nature, which make it hard to capture using single off the shelf
model architectures. In this work, we present SuperCone, our unified
predicative segments system that addresses the above challenges. It builds on
top of a flat concept representation that summarizes each user's heterogeneous
digital footprints, and uniformly models each of the prediction task using an
approach called "super learning ", that is, combining prediction models with
diverse architectures or learning method that are not compatible with each
other. Following this, we provide an end to end approach that learns to
flexibly attend to best suited heterogeneous experts adaptively, while at the
same time incorporating deep representations of the input concepts that
augments the above experts. Experiments show that SuperCone significantly
outperform state-of-the-art recommendation and ranking algorithms on a wide
range of predicative segment tasks and public structured data learning
benchmarks.

研究用户分割问题，通过 SuperCone 系统中的数字足迹，使用超级学习方法建立预测模型，灵活地 “适应” 异构专家来实现用户分割，并在实验证明此方法的优越性。