# Trustworthiness of mathbbX Users: A One-Class Classification Approach

arXiv: https://arxiv.org/abs/2402.02066 · HTML: https://mlsanity.com/paper/2402.02066/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [Subspace Support Vector Data Description](https://mlsanity.com/paper/1802.03989/) (2018)
- [Multimodal Subspace Support Vector Data Description](https://mlsanity.com/paper/1904.07698/) (2019)
- [Ellipsoidal Subspace Support Vector Data Description](https://mlsanity.com/paper/2003.09504/) (2020)
- [Graph-Embedded Subspace Support Vector Data Description](https://mlsanity.com/paper/2104.14370/) (2021)
- [Boosting rare benthic macroinvertebrates taxa identification with one-class classification](https://mlsanity.com/paper/2002.10420/) (2020)
- [Early Myocardial Infarction Detection with One-Class Classification over Multi-view Echocardiography](https://mlsanity.com/paper/2204.07253/) (2022)
- [Newton Method-based Subspace Support Vector Data Description](https://mlsanity.com/paper/2309.13960/) (2023)
- [Credit Card Fraud Detection with Subspace Learning-based One-Class Classification](https://mlsanity.com/paper/2309.14880/) (2023)
- [Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter](https://mlsanity.com/paper/1410.4139/) (2014)
- [Seeing and Believing: Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2107.08027/) (2021)
- [Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users](https://mlsanity.com/paper/2210.15214/) (2022)

## Built on by (most cited first, 2 of 2)

- [Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach](https://mlsanity.com/paper/2502.15793/) (2025)
- [To Vaccinate or not to Vaccinate? Analyzing mathbbX Power over the Pandemic](https://mlsanity.com/paper/2503.02563/) (2025)

## Abstract (arXiv)

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.