## 4 Performance Evaluation Results and Discussion
### Performance Evaluation for User Activity  
The 55,452 users who were evaluated in the restaurant category were used to verify the performance of the user activity. For all 55,452 users, the user activity was calculated based on the Yelp data attributes that map to the components of the user activity formula. The \(\alpha\) and \(\beta\) configurable parameters were again all set to 1, which means that the weights for each component were equally set to 1 and the components were not normalized, to see how the component data characteristics would be affecting the activity. Among these users, the top 10 active users were selected. Table 3 shows these top 10 active users as well as some of the lowest 5 active users. The user ID, calculated user activity, calculated user influence, number of elites, number of fans, number of reviews, and the recent review date are shown. These top 10 active users' evaluations of restaurants were then compared against the average evaluation of all users on the same restaurants. If the evaluations of the top 10 active users are similar with the average of all user evaluations, the top 10 active users may be considered to provide credible evaluations. Figure 5 shows a comparison graph for each of the top 10 active users. Each of the top 10 active user evaluations were compared against the average evaluation of all users for each of the 10 or less restaurants most recently reviewed by each of the top 10 active users. The restaurants are denoted as items in the graphs. The graphs show that the evaluation given by the top 10 active users are generally similar to the average evaluations of all users in terms of the increase and decrease pattern trends. However, the evaluations by the top 10 active users show some strong opinions in the form of spikes occurring at some restaurants by giving very high evaluations for some restaurants that are moderately high evaluated by all the users, or by giving very low evaluati  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rank** & **User ID** & **Influence** & **Activity** & **Num** & **Num** & **Num** & **Num** & **Recent Date** \\ \hline Top 1 & w6 Vv-kidGpmVSGqkVTbAQ & 690.19616 & 220.88925 & 6 & 1172 & 459 & 5 & 22 February 2011 \\ \hline Top 2 & sEDDGC8 LNHooDmPHmj-mg & 633.01554 & 73.3  
that are moderately low evaluated by all the users. This could be understood from the characteristics of an active user, who will actively provide reviews that suggest strong opinions in the form of very good or very bad to gain constant interest from the social network community. This kind of spike in the data pattern creates a little more difference for some points compared to the user influence graphs. This shows that the top 10 active users are providing an evaluation that has a generally close pattern to the overall users but may show stronger opinioned evaluations in some cases. Figure 3: Comparison of the evaluations by the top 10 influential users with the average evaluations by all users. Figure 6 shows the difference between the evaluations by the top 10 active users and the average of evaluations by all users. Figure 6a shows the evaluation difference for each of the restaurants. The evaluation difference shows a little bit more fluctuation than the user influence case but is still reasonably close to 0 for all the restaurants. This means that, even for different restaurants, the top 10 active users do not display significantly big fluctuations and maintain a consistent credibility. Figure 6b shows that the average of the difference for each top 10 active users is within the +1 or \(-1\) range. The minor fluctuations seem to be disappearing when calculating the average difference among the restaurants. In other words, the strongly opinioned evaluations by active users are sometimes very high but other times very low and are cancelled out when calculating the average of the differences. This shows that the top 10 active users can provide reasonably credible evaluations for restaurants with occasional stronger opinioned evaluations.