## 5 Results
### Statistical Analysis of Recommendation Results  
To observe the results of our recommendation module, we used ground truth data. This data includes 100 projects, 55 non-scams, 20 suspended projects, ten canceled projects, and 15 successfully funded projects. Our proposed model works efficiently with high accuracy in both scenarios, i.e., when projects are from the same category or different categories. This dataset exemplifies all possible use case scenarios. We can evaluate how well our recommendation system performs on each type of project in terms of its funding status. The above Figure 4 shows the percentage for each category of crowdfunding projects. These types are categorized based on the funding status of a project, i.e., \"Non-Scam\" are projects that are successfully delivered after successful funding; \"successfully funded scam\" are projects that successfully raised the required funds but failed to deliver; \"canceled\" category represents projects that have been withdrawn by the project creator before its funding period expires; and \"suspended\" type means those projects which have been discontinued by the platform in case they figure out any suspicious activity or content. We have tested our model on all the categories mentioned above of projects to find their authenticity. In Figure 5, we have estimated the authenticity levels for the suspended projects. The \(x\)-axis shows \begin{table}
\begin{tabular}{c c c} \hline \hline
**Decision** & **Credibility Level of a Project** & **Range of Authenticity (Score)** \\ \hline
**Highly undesired** & Extremely Low & (\(>\)0 \(\leq\) 0.2) \\ \hline
**Not desired** & Low & (\(>\)0.2 \(\leq\) 0.4) \\ \hline
**Can be considered** & Normal & (\(>\)0.4 \(\leq\) 0.6) \\ \hline
**Desired** & High & (\(>\)0.6 \(\leq\) 0.8) \\ \hline
**Highly Desired** & Extremely High & (\(>\)0.8 \(\leq\) 1.0) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Selection Criteria for Credibility. Figure 4: Percentage of crowdfunding projects for each category. the authenticity levels between 0 and 1, and the \(y\)-axis presents the percentage of suspended projects. The estimated authenticity level for 93% of the suspended projects falls in the range (0-0.2). The data used for this experiment included 80 suspended projects, and 74 projects were falling into the highly undesired range of credibility. This means that these projects are highly undesirable for backers. That is true because these projects are being suspended for some suspicious activities. From this range, we can interpret that most of the projects that have been suspended fail to fulfill the selection criteria of the credibility assessment tool for the recommendation. The statistical analysis of the results is presented in Table 8.  
In Figure 6, we have evaluated the authenticity levels for 70 canceled projects. The estimated authenticity level for 84% of the canceled projects falls in the range (0.4-0.6). Hence, keeping the risk factor in mind, these projects can be considered for investments. The statistical analysis is presented in Table 9. These projects are canceled for multiple reasons, such as lack of funding and budget issues during development phases. The results show that for most cases, the predicted authenticity level range is between 0 and 0.2. We used 120 undelivered projects, i.e., successfully funded but never delivered projects, and out of these selected projects, 112 projects did not meet the credibility criteria and are highly undesired projects. It represents that regardless of successfully raising funds, backers are disappointed with the progress and development. For such projects, comments play a vital role in understanding a creator's behavior towards his investors after successfully collecting the desired funds. False promises, long delays in communication, or disappearance from the platform are the essential characteristics found in such cases. Figure 5: Authenticity estimation for suspended projects.