系统梳理社交网络中信任建模的算法与应用，助力识别虚假信息。
A Survey on Quantitative Modeling of Trust in Online Social Networks
- 按算法基础分类主流信任模型，分析其构建机制
- 总结可用数据集、特征与典型应用场景
- 适合研究网络信任、反虚假信息的学者参考
在线社交网络促进用户参与和信息共享，但也充斥着虚假信息和欺骗行为。信任建模研究致力于开发计算模型或算法，以度量信任关系、评估内容可靠性并检测垃圾信息或恶意活动。然而，现有综述多仅简要提及信任概念，或聚焦单一类别模型。本文全面梳理了面向在线社交网络的前沿信任模型。首先，探讨心理学中信任相关理论，识别影响线上信任形成与演化的关键因素；其次，基于算法基础对最新信任模型进行分类，剖析各类模型的建模机制及其在量化信任方面的独特贡献；随后，提供一份以实现为导向的信任建模手册，涵盖可用数据集、信任相关特征、有前景的建模技术及可行应用方向；最后，总结文献成果，并讨论尚未解决的挑战。
原文摘要 · Abstract (English)
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.
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