## 7 Discussions and Concluding Remarks  
This section emphasizes the challenges, implications, and limitations in the development of the proposed recommendation system in crowdfunding field. In this study, an optimized recommendation system is developed to help investors in the selection process of authentic and reliable projects by accommodating their interests. We have presented a hybrid model of RNN-LSTLM and topic modeling that combine the benefits of both LSTMs, which can capture time dependencies for the topic and class prediction, and topic modeling, which can extract topics that can help predict scams. Our proposed model has improved the accuracy of scam prediction significantly from the baseline LDA-based models. We have also embedded an optimized recommendation strategy based on a project's credibility. There were several challenges in the development of the proposed system. One of the key challenges was collection of ground truth data, and its verification, was a tedious task. As there is no public data available for scam cases in crowdfunding, so we hand-collected data from the most  
Figure 11: Discussion trends over time in suspicious campaigns based on topic classes. substantial reward-based and leading crowdfunding platform--Kickstarter. Additionally, all the relevant data and clues were cross-checked manually several times, which was quite a time taking task. The primary goal of our recommendation systems is to determine the credibility of a project before recommending it to the investors by incorporating their preferences. Online markets, e-commerce, and all form of online business exchanges involve risks and uncertainty. Similarly, for crowdfunding platforms, it is very crucial to establish or build investors' trust to curtail the chances of uncertainty and risky investments. Previous studies show that many elements are vital for trust-building in any domain, such as content quality and readability [49] and presence of profile picture [50]. Therefore, to determine the credibility of a project, we evaluate essential elements such as the profile of the creator, his or her communication patterns and sentiments of investors. The projects with highest credibility level are safe to recommend. However, in case of crowdfunding projects, the amount of risk an investor can tolerate becomes subjective. If an investor is not concerned about the money and only interested in the idea, he or she can invest money to bring that idea to life. Therefore, we present the authenticity scores of projects and evaluate their credibility accordingly. The rest is in the investor's hand to make a decision accordingly. Some theoretical and practical contributions of the study can be summarized as follows. First, our results complement the previous studies [42; 43] and show that the hybrid approaches perform better in topic identification. With LSTM-LDA, we were able to achieve 96% accuracy in topic predictions. We also evaluated these topic classes to help identify suspicious campaigns. Second, we investigated to discover how configuring comments along with project timelines can help improve the prediction quality. For this experiment, we divided the comments into five different batches described in Table 7. Each batch represents a specific timeline, such as batch 1 represents the period between the campaign launched until fundraising. Similarly, other batches are created based on different development phases of a project. There are 50-70 comments in each batch. Projects with less than 50 comments were dropped out for this experiment. From Table 7, we can observe that the most recent comments in a project are significant towards an accurate classification of scam or non-scams. Additionally, the comments discovered in the latest batch, i.e., batch 5, are pure towards one topic class. In other words, they are less likely to have a mixture of discussions as people are generally sharing the same thoughts in recent comments.