  $\mathbb{X}$ (formerly Twitter) is a prominent online social media platform
that plays an important role in sharing information making the content
generated on this platform a valuable source of information. Ensuring trust on
$\mathbb{X}$ is essential to determine the user credibility and prevents issues
across various domains. While assigning credibility to $\mathbb{X}$ users and
classifying them as trusted or untrusted is commonly carried out using
traditional machine learning models, there is limited exploration about the use
of One-Class Classification (OCC) models for this purpose. In this study, we
use various OCC models for $\mathbb{X}$ user classification. Additionally, we
propose using a subspace-learning-based approach that simultaneously optimizes
both the subspace and data description for OCC. We also introduce a novel
regularization term for Subspace Support Vector Data Description (SSVDD),
expressing data concentration in a lower-dimensional subspace that captures
diverse graph structures. Experimental results show superior performance of the
introduced regularization term for SSVDD compared to baseline models and
state-of-the-art techniques for $\mathbb{X}$ user classification.
