# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 1 Introduction
Secondly, lesser types of data from OSN users are permitted to use, given the fact that the privacy and data protection of online users are being much more respected nowadays. Online service users may opt out of using some of or all of their data for certain data analysis tasks conducted by online services--especially with the regulations and laws being implemented and enforced, such as General Data Protection Regulation (EU) (GDPR) [22], the California Consumer Privacy Act (CCPA) [23], and the Personal Information Protection Law of the People's Republic of China (PIPL) [24]. Thus, some crucial data may be unobtainable for trust inference. Last but not least, most trust inference models are poorly interpretable. The interpretability of a trust inference model should be improved alongside achieving better performance, not only for inferring trust itself but for making other relevant informed decisions. Bearing the aforementioned problems in mind, we propose creating a probabilistic graphical model in which various types of UGC data are built and then integrated as features in such a way that not only are most of their characteristics preserved, but that the interaction among features can also be captured and embedded. The contributions of this paper are as follows. * The proposed model takes advantage of the integration of the trust network and user-generated contents in the network; the latter is embedded into a probabilistic graphical model built upon the former. The model permits the directionality of trust relationships and preserves various facets and properties of trust. The way of both building features from UGC data and embedding them into the probabilistic graph preserves as much information as the data may contain. * To infer trust, the proposed model uses a message passing algorithm, loopy belief propagation, for the model's probabilistic inference. This inference algorithm can be viewed as a reproduction of the propagative and incomplete transitive characteristics of trust. By using the message passing algorithm, the resulting probability for each predicted user-to-user trust relationship can be well interpreted. * As a binary classification task, the performance of the proposed method to infer trust is demonstrated with a dataset derived from a real online social network in comparison with some state-of-the-art binary classifiers. Experimental results show the proposed model achieves better accuracy and F\({}_{1}\) score with the whole data presented and maintained higher recall and acceptable precision with some of data absent. Thus, one can conclude that the proposed model shows its promising ability for trust inference in nowadays privacy-constrained online social analysis where available data are often limited. To address the data limitation, the problem that a model should have higher precision or higher recall is also discussed. Although this paper only focuses on inferring user trust in online social networks, the proposed model may also be adopted to fulfill other inference tasks to better assist decision making in other complex systems. The least work required for other similar tasks is to properly define a concrete graphical model and a set of reasonably-built features, if additional data exist. The rest of the paper is organized as follows. In Section 2, we briefly review the literature of related works on trust inference. In Section 3, we elaborate the prerequisites, define the problem, and then propose the model. In Section 4, we present experiments conducted with a dataset derived from a real-world online social network, and then analyze the performance of the proposed model. Finally, we conclude our work in Section 5.