## 5 Discussion  
The findings indicate that while the all-MiniLM-L6-v2 embedding provides superior performance in terms of NPMI, it may require additional computational resources, particularly as the number of topics increases. In contrast, the BAAI/bge-base-en-v1.5 and BAAI/bge-small-en-v1.5 embeddings, while less effective in capturing semantic relationships, offer improved efficiency, making them suitable for applications where computational resources are limited. One of the key observations is the impact of embedding model size on computational efficiency and topic coherence. Larger models, such as BAAI/bge-large-en-v1.5, tend to achieve higher coherence in some cases but come at the cost of significantly longer computation times. This trade-off highlights the need for careful consideration of the specific requirements of e-participation platforms. For instance, platforms operating in resource-constrained environments may prioritize smaller, more efficient embeddings, even if it means sacrificing some degree of semantic accuracy. The e-participation platforms Join and iVoting, while complementary in purpose, differ significantly in their scope, functionality and deployment. Join, introduced in February 2015 by the National Development Council, was designed to foster whole-citizen participation by facilitating comprehensive civic engagement [13]. It allows users to propose, discuss, and vote on policy issues, as well as provide feedback on draft legislation. In contrast, iVoting--launched during the 2012 Legislative Yuan elections--focuses specifically on enhancing the electoral process through informed voting. As Taiwan's first Voting Advice Application (VAA), iVoting helps users make more deliberate and informed decisions by offering issue position diagnostics. An additional point of interest is the effect of multilingual datasets on topic coherence. While [PERSON] et al. [13] highlight that little is known about the participation platform Join due to language barriers, our translation pipeline demonstrates that efficient NLP applications can help analyze large volumes  
Figure 12: Confusion matrix showing the classification performance of the model across different topics. The matrix highlights true positives, false positives, true negatives, and false negatives for each class, providing insights into areas of misclassification, particularly in overlapping topics. of proposals in Chinese and synthesize their thematic content. The major discourse on Join and iVoting has been extracted. It could be seen that both platforms have common topics that are of concern for their users. However, the translation of proposals into English before applying neural topic models may introduce noise, potentially lowering the coherence scores. This effect warrants further investigation, particularly in platforms with high linguistic diversity. Future studies could explore techniques such as fine-tuning multilingual embeddings to better handle translation artifacts and maintain semantic integrity across languages. The temporal analysis of topics as can be seen in Figure 5 also provides valuable insights into the evolving interests of users on e-participation platforms. For example, spikes in topics related to public health, such as COVID-19 responses, align with significant global events, underscoring the potential of such platforms to reflect and adapt to real-time societal needs. Understanding these shifts can help policymakers identify emerging concerns and allocate resources more effectively. Moreover, the results emphasize the importance of confidence intervals in evaluating model performance. The variability observed in models like BAAI/bge-small-en-v1.5 suggests that embedding stability across multiple trials is a critical factor in determining their suitability for deployment. By incorporating robustness checks, practitioners can ensure more reliable and interpretable results in their analyses.