Trustworthiness of $\mathbb{X}$ Users: A One-Class Classification Approach
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914664667938816 |
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| author | Khan, Tanveer Sohrab, Fahad Michalas, Antonis Gabbouj, Moncef |
| author_facet | Khan, Tanveer Sohrab, Fahad Michalas, Antonis Gabbouj, Moncef |
| contents | $\mathbb{X}$ (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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Trustworthiness of $\mathbb{X}$ Users: A One-Class Classification Approach Khan, Tanveer Sohrab, Fahad Michalas, Antonis Gabbouj, Moncef Social and Information Networks Artificial Intelligence $\mathbb{X}$ (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. |
| title | Trustworthiness of $\mathbb{X}$ Users: A One-Class Classification Approach |
| topic | Social and Information Networks Artificial Intelligence |
| url | https://arxiv.org/abs/2402.02066 |