Trustworthiness of $\mathbbX$ Users: A One-Class Classification Approach
<|reference_start|>Trustworthiness of $\mathbbX$ Users: A One-Class Classification Approach: $\mathbb{X}$ (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.<|reference_end|>
@article{khan2024trustworthiness,
    title={Trustworthiness of $\mathbb{X}$ Users: A One-Class Classification
  Approach},
    author={Tanveer Khan, Fahad Sohrab, Antonis Michalas, Moncef Gabbouj},
    journal={arXiv preprint arXiv:2402.02066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02066},
    primaryClass={cs.SI cs.AI}
}