# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 3 The Proposed Model
### Model Construction
#### 3.2.2 Features  
As stated in Assumption 1, trust information can be obtained, and trust can be harvested and evaluated through a user's generated contents in online social networks, including reviews, posts, connections, etc., as they are the representation and bearer of the user's attitude, credibility and trustworthiness, i.e., the trust constructs. Therefore, we extract various types of features from the dataset and embed them to the probabilistic graphical model to aid trust inference. In our conditional random field, an arbitrary number of features of any arbitrary type can be attached to any node or any edge. All the features used in the proposed model are discrete, and they can be _label-observation features_ for nodes, _label-label-observation features_ for edges or _label-label features_ for edges. In other words, the feature function of each feature is non-zero for a single state per node or a single state per edge (the state of an edge is determined by the state-pair of the two nodes connected by the edge), and the type and value of the feature are derived from observations in the UGC data from the OSN dataset. For example, we observe in the OSN dataset that a user has 57 trustors and the corresponding user node will be associated with a feature, whose type is \"nTrustors\" and value is 57. Table 2 lists typical sets of features used in this paper and we briefly describe them below. Statistical features for user profiles (User profile features)  
A user's profile is the most direct depiction of the user's social capital that reflects their identity and status which in turns reflect their attitude, credibility and trustworthiness. A set of typical statistical features for user profiles built from UGC data are used in this paper. As [43] suggests, an Internet celebrity, who is in fact an active and vigorous source for disseminating information, usually have a great many followers or trustors. Thus, the _number of trustors_ of a user is an obvious indicator of the evidence that how the user's being trusted by others. In the meantime, the _number of trustes_ of a user also shows their engagement and importance in the online social network. The _number of reviews_ posted by a user and the _number of ratings_ cast by a user is a reflection of their experience, frequency and involvement in the online social network. Some online social services also have a rank system for user reviews' helpfulness, and the _numbers of a user's reviews' helpfulness being rated_ as _exceptional helpful_, _very helpful_, _helpful_, _somewhat helpful_ or _not helpful_ are intuitive hints for the user's experience, expertise and credibility. In the proposed model, for each user, we build the statistical features from the user's profile data and attach them to the user node; for each trust relationship, we build the statistical features from the two involving users' profiles as edge features and attach them to the corresponding edge between the user node and the trustRelation node. As explained previously, an edge between a user node and a trustRelation node may have two different types. Herein, for either type of such edges, each user profile feature has a designated type, either as a feature denoted by \"u2 TrU\" for edges between a trustor node and a trustRelation node, or as a feature denoted by \"Tr2 uU\" for edges between a trustRelation node and a trustee node. In this way, the model is guaranteed to distinguish the features for a user as a trustor in a trust relationship from those features for the same user acting as a trustee in another trust relationship, which conforms to our Assumption 2. **Feature vector construction. ** Using features listed in Table 3: