# User Trust Inference in Online Social Networks: A Message Passing Perspective
## 4 Experiments
### Results and Discussion  
#### 4.3.1 The First Set of Experiments with All Possibly Usable Feature Data  
Performance of the proposed method with different feature sets  
Firstly, we report the trust inference performance of the proposed method. The experiments were conducted with eight different feature set combinations on the five split training and test datasets, and accuracy and \(\mathbf{F_{1}}\) score are reported for each of them. As shown in Figure 6, the results are organized into three groups by how the feature sets were composed: the first group contains results from experiments 1, 2, 5, 6, and 8, which illustrates how integrating the user profile feature set \(\mathcal{F}_{\text{PRF}}\) into the model affects the model's performance; similarly, the second group contains experimental results from experiments 1, 3, 5, 7, and 8 to show the power of using UGC feature set \(\mathcal{F}_{\text{UGC}}\); the third group of 1, 4, 6, 7, and 8 depicts the effect of trust propagation feature set \(\mathcal{F}_{\text{TP}}+\mathcal{F}_{\text{TPALX}}\). With either a single feature set or a combination of feature sets, the proposed method outperformed three naive classifiers, _uniformly random guess_, _random selected class_, and _majority class_, which, respectively, achieved accuracies of 0.5000, 0.5014, and 0.5269 according to the dataset specification (refer to Table 7). From the reported results evaluated by the accuracy metric, one can conclude that the proposed method is efficient and capable of inferring trust relationships. As was previously discussed, with only the first category of auxiliary features, the proposed method will still worked, and it also achieved above-average performance. They are special features that only work with our model but not for other comparison methods. This makes our model with this set of features promising as a supervised learning model, when abundant data, such as user-generated content, are available as data input. A quick glance over the performance results regarding both accuracy and F\({}_{1}\) score shows that substituting additional features into the model does improve the model's performance. However, the results differ a bit when the added combination of additional feature sets varies. The observations are as follows. 1. On top of the first category of auxiliary features (\(\mathcal{F}_{\text{TAUX}}\)), adding a single feature set (\(\mathcal{F}_{\text{PRF}}\), \(\mathcal{F}_{\text{UGC}}\), or \(\mathcal{F}_{\text{TP}}+\mathcal{F}_{\text{TPALUX}}\)) into the model will improve the model's performance. The use of the UGC feature set \(\mathcal{F}_{\text{UGC}}\) improves the model's accuracy (and F\({}_{1}\) score) greatly by 42.45% to 50.22% (27.80% to 30.76%), followed by the user profile feature set \(\mathcal{F}_{\text{PRF}}\) by 14.92% to 20.87% (1.16% to 6.60%), and then the trust propagation feature set \(\mathcal{F}_{\text{TP}}+\mathcal{F}_{\text{TPALX}}\) by 0.35% to 1.01% (0.34% to 1.32%). 2. Although adding a single feature set \(\mathcal{F}_{\text{TP}}+\mathcal{F}_{\text{TPALX}}\) did not greatly improve the performance, it could help another single feature set \(\mathcal{F}_{\text{PRF}}\) to achieve better results. This can be seen from experiment 6 and experiment 2, where the model performance was improved by 11.26% to 14.27% in accuracy (Figure 5(a)) and 13.60% to 18.09% in F\({}_{1}\) score (Figure 5(d)). 3. Using all types of features (in experiment 8) does not always promise the best result. The performance for the proposed model with such feature sets was close to the