# A Survey on Quantitative Modeling of Trust in Online Social Networks

arXiv: https://arxiv.org/abs/2603.11054 · HTML: https://mlsanity.com/paper/2603.11054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 184 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Graph Neural Networks in Recommender Systems: A Survey](https://mlsanity.com/paper/2011.02260/) (2020)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [IPFS - Content Addressed, Versioned, P2P File System](https://mlsanity.com/paper/1407.3561/) (2014)
- [Scalable and Generalizable Social Bot Detection through Data Selection](https://mlsanity.com/paper/1911.09179/) (2019)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [TwiBot-22: Towards Graph-Based Twitter Bot Detection](https://mlsanity.com/paper/2206.04564/) (2022)
- [Explainable Goal-Driven Agents and Robots -- A Comprehensive Review](https://mlsanity.com/paper/2004.09705/) (2020)
- ["Dave...I can assure you...that it's going to be all right..." -- A definition, case for, and survey of algorithmic assurances in human-autonomy trust relationships](https://mlsanity.com/paper/1711.03846/) (2017)
- [Unsupervised Social Bot Detection via Structural Information Theory](https://mlsanity.com/paper/2404.13595/) (2024)
- [MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark](https://mlsanity.com/paper/2301.01123/) (2023)
- [VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media](https://mlsanity.com/paper/2110.15454/) (2021)
- [Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods](https://mlsanity.com/paper/2205.00189/) (2022)
- [TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support](https://mlsanity.com/paper/2306.13339/) (2023)
- [Detecting False Rumors from Retweet Dynamics on Social Media](https://mlsanity.com/paper/2201.13103/) (2022)
- [TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature](https://mlsanity.com/paper/2205.12784/) (2022)
- [Profiling Fake News Spreaders on Social Media through Psychological and Motivational Factors](https://mlsanity.com/paper/2108.10942/) (2021)
- [Adaptive Hypergraph Network for Trust Prediction](https://mlsanity.com/paper/2402.05154/) (2024)
- [Trust Assessment in Online Social Networks](https://mlsanity.com/paper/1909.10066/) (2019)
- [Username Squatting on Online Social Networks: A Study on X](https://mlsanity.com/paper/2401.09209/) (2024)

## Abstract (arXiv)

Online social networks facilitate user engagement and information sharing but are also rife with misinformation and deception. Research on trust modeling in online social networks focuses on developing computational models or algorithms to measure trust relationships, assess the reliability of shared content, and detect spam or malicious activities. However, most existing review papers either briefly mention the concept of trust or focus on a single category of trust models. In this paper, we offer a comprehensive categorization and review of state-of-the-art trust models developed for online social networks. First, we explore theories and models related to trust in psychology and identify several factors that influence the formation and evolution of online trust. Next, state-of-the-art trust models are categorized based on their algorithmic foundations. For each category, the modeling mechanisms are investigated, and their unique contributions to quantitative trust modeling are highlighted. Subsequently, we provide an implementation-centric trust modeling handbook, which summarizes available datasets, trust-related features, promising modeling techniques, and feasible application scenarios. Finally, the findings of the literature review are summarized, and unresolved challenges are discussed.