# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 3 The Proposed Model
### Model Construction
#### 3.2.2 Features
\begin{table}
\begin{tabular}{c l} \hline \hline
**Feature Set** & **Description of Features in the Set** \\ \hline \(\mathcal{F}_{\text{PRF}}\) & Statistical features for user profiles (User Profile Features) \\ \(\mathcal{F}_{\text{UGC}}\) & Linguistic and stylistic features for reviews (UGC Features) \\ \(\mathcal{F}_{\text{TP}}\) & Propagative features for trust propagation (TP Features) \\ \(\mathcal{F}_{\text{TAUX}}\) & The first category of Auxiliary features \\ \(\mathcal{F}_{\text{TPaux}}\) & The second category of Auxiliary features \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sets of features used in the proposed model. * For each user U\({}_{u}\), we create a feature vector \(F^{\text{U}}_{\text{PRF}}(\text{U}_{u})\). Each feature of this type is a _label-observation feature_. * For each trustor \(\leftrightarrow\) trustRelation edge or trustRelation \(\leftrightarrow\) trustee edge, we create a feature vector \(F^{\text{u2 TW}}_{\text{PRF}}(\text{E}_{u\leftrightarrow t})\) or \(F^{\text{T2 uU}}_{\text{PRF}}(\text{E}_{t\leftrightarrow u})\), respectively. Each feature of this type is a _label-label-observation feature_. Linguistic and stylistic features for reviews (UGC features)  
As previous studies [6, 39, 40] suggest, the linguistic characteristics and stylistic features of a review deliver the attitude, emotional status and part of expertise of the author, the quality of it implies whether or not the author is objective and unbiased, and the textual content of the review conveys the author's experience and expertise. According to Assumption 1, features extracted from reviews contribute to each user's attitude and trustworthiness, and thus affect their probability of trusting others and being trusted by others. Furthermore, according to Assumption 3, investigating this type of features also helps in finding similar users and suggesting trust relations to similar users. The linguistic and stylistic features for posts used in this paper include:  
* _Parts-of-speech_ (POS) used in this paper include nouns, verbs, adjectives, adverbs and conjunctions. These POS are mostly-used classes of words and may have different impacts across reviews. We use the ratio of the number of words in each POS type to the number of segments in a review as the feature value. * The _Subjectivity_ and _Polarity_ of a word or a phrase describes whether the segment expresses either a _positive_ or a _negative_ meaning in either _strong_ or _weak subjective_ way. These words can have various parts-of-speech. We use the ratio of the number of these words or phrases to the number of segments in a review as the feature value. * _Indicative_ words could imply whether a post will be more credible or less convincing. They're functioning as _assertives, factives, implicatives, report verbs, hedges_ or _biased words_. The lexicons are from [40, 44]. Similarly, we use the ratio of the number of these words to the number of segments in a review as the feature value. The _Affective words_ ([[http://vndomains.fbk.eu/vnaffect.html](http://vndomains.fbk.eu/vnaffect.html)]([http://vndomains.fbk.eu/vnaffect.html](http://vndomains.fbk.eu/vnaffect.html)) (accessed on 1 June 2021)) and _Sentimental words_[45], which express an author's emotions, traits, sensations, attitudes or behaviors, can also be served as features for reviews. Using them may slightly increase the model's performance, however, for the sake of simplicity, we do not leverage them as features in this work. All UGC features are attached to edges between user nodes and trustRelation nodes. Similarly as how we did with user profile features, we also mark UGC features with either type \"u2 TrR\" or type \"Tr2 uR\" to respect the two distinct types of edges to which they are attached. **Feature vector construction. ** Using features listed in Table 4: