###### Abstract  
E-participation platforms, such as iVoting and join in Taiwan, provide digital spaces for citizens to engage in deliberation, voting, and oversight. As a forerunner in Asia, Taiwan has implemented these platforms to enhance participatory democracy. However, there is still limited research on the specific content debated on these platforms. Utilising recent advancements in Natural Language Processing, the content of proposals that users have submitted between 2015 and 2025 is explored. In this study, a pipeline for mining text corpora scraped from these platforms in the context of political analysis is proposed. The pipeline is applied to two datasets which have different characteristics. A topic model for each of the two platforms is generated and later evaluated with OCTIS (Optimizing and Comparing Topic Models Is Simple) and compared to different baselines. Our research highlights the trade-offs between model performance and processing time, emphasizing the balance between accuracy and meaningful topic creation. By integrating a translation pipeline from Chinese to English within the text-mining process, our method also demonstrates a solid approach to overcome language barriers. Consequently, our method is adaptable to e-participation platforms in various languages, providing decision-makers with a more comprehensive tool to understand citizens' needs and enabling the formulation of more informed and effective policies. Keywords:policy informatics; text mining; neural topic modeling; e-participation +
Footnote †: _Article_  
law proposals, and e-voting. While digital participation can enhance time efficiency and accessibility [7; 8], researchers also highlight that technical barriers, limited digital literacy, and a lack of emotional engagement can have an inhibiting effect, as examples from the COVID-19 pandemic have demonstrated [9]. In America, researchers who investigated the We the People e-petition platform have found that it expresses short-winded policy debates after major sociopolitical events, fueled by media coverage and social media mobilization [1]. In Taiwan, [PERSON] et al. [10] have examined the iVoting platform and its impact on digital democratic development, emphasizing that the challenge of informed voter participation, along with the growing online presence of political parties and their internet-savvy supporters, is reflected in petition outcomes. Equally critical is the ability to track users who abandon discussions, particularly in Taiwan's cyberspace, which has ranked as the number one country regarding the exposure of disinformation [11]. In addition to the voting-based platform iVoting, our study also examines the proposals on Join, as this platform provides more space for deliberation and has become the central e-participation platform in recent years. On [PERSON]'s ladder, Join spans consultation and partnership, as citizens can discuss and vote for proposals from other users and governmental bodies. Similarly, on both platforms, legislators are required to address proposals once they reach a certain number of votes. Meanwhile, iVoting has seen a significant decline in usage in recent years. The platform was primarily designed for Taipei City to vote on municipal affairs. In contrast, Join serves as a national platform managed by the central government. To extract information easily and make it publicly available, more machine-aided tools and research is necessary [12]. This research employs a Natural Language Processing (NLP) method to assist policymakers and administrators in gaining a comprehensive understanding of uncategorized online proposals. While existing studies on Join and iVoting have focused primarily on petition dynamics [13], this study extends the analysis to thematic shifts and topical trends over a ten-year period within the framework of policy informatics.