Trustworthiness of mathbbX Users: A One-Class Classification Approach

mathbbX (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 mathbbX is essential to determine the user credibility and prevents issues across various domains. While assigning credibility to mathbbX 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 mathbbX 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 mathbbX user classification.