Supporting Online Discussions: Integrating AI Into the adhocracy+ Participation Platform To Enhance Deliberation (2025)

Abstract: Online spaces provide individuals with the opportunity to engage in discussions on important topics and make collective decisions, regardless of their geographic location or time zone. However, without adequate support and careful design, such discussions often suffer from a lack of structure and civility in the exchange of opinions. Artificial intelligence (AI) offers a promising avenue for helping both participants and organizers in managing large-scale online participation processes. This paper introduces an extension of adhocracy+, a large-scale open-source participation platform. Our extension features two AI-supported debate modules designed to improve discussion quality and foster participant interaction.In a large-scale user study we examined the effects and usability of both modules. We report our findings in this paper. The extended platform is available at https://github.com/mabehrendt/discuss2.0.

## introduction online discussions and participation platforms enable people to engage in socially relevant issues. however, written exchanges in online spaces are frequently marked by a lack of structure, often leading to information overload, making it difficult for both participants and providers to process large volumes of contributions @xcite . according to @xcite , other key issues include polarization, incivility, toxic behavior, superficial content, and insufficient collaboration among participants. to address these challenges, the concept of deliberation proves particularly valuable. deliberation is defined as the respectful and argumentative exchange of opinions aimed at making a decision. it encompasses three core dimensions: rationality, referring to the argumentative exchange of opinions; civility, which entails politeness and respect; and reciprocity, characterized by responsiveness and active listening @xcite @xcite . ai presents a promising opportunity to enhance deliberation, supporting both participants and organizers in creating a more structured, respectful, and engaging environment for meaningful exchange of opinions. in this work, we propose two aibased solutions to improve online discussions, implemented for adhocracy+, an open-source participation platform. ## related work previous efforts to integrate ai into discussion platforms have often focused on structuring and summarizing discussions. the consul foot_0 citizen participation tool enables citizens to propose ideas to local politicians on improving their city. these proposals can be supported and discussed by other participants on the platform. to address the issue of information overload, arana-catania et al. (2021) improved the platform with several natural language processing (nlp) methods, including tools to summarize existing proposals, automatically categorize them and recommend proposals to participants according to their interests. in the kosmo project, an ai-supported moderation dashboard was developed for the adhocracy+ platform to assist moderators during citizen participation processes 2 . two models were trained to identify uncivil and engaging comments @xcite , which are flagged for moderators, allowing them to decide on appropriate actions, such as blocking uncivil comments. the bcause platform, created by @xcite , supports discussions with an automatic text summarization tool and an argument recommendation system. this system suggests arguments from scientific literature based on the user's stance on the discussed topic. other examples of open-source discussion tools that incorporate ai features include discourse 3 and polis @xcite from the computational democracy project. another notable example is community-pulse @xcite , a platform equipped with tools for text analysis and visualization to help civic leaders to make sense of community input. it includes a sentiment analysis of contributions and topic modeling to automatically extract discussed topics. ## features in the following, we will discuss the features of the two implemented modules from both a technical and a user perspective. deliberative quality score stance detection imports imports debate module response (stance or quality score) ai tools django debate model comment signals.py added comment event debate view debate module display comments figure 2: overview of the architecture to extend adhocracy+ with our ai tools. (left) the debate module imports both the stance detection and deliberative quality ai's as python modules. (right) the django database model sends out an event when a new comment is added to the database. the event is handled in signals.py where the new comment is passed either to the stance detection or deliberative quality model. these send a response (either a stance or quality score) back to the database where the corresponding response is stored. ## implementation details adhocracy+ is built on the django framework foot_2 and provides a wide range of functionalities and modules to facilitate large-scale online discussions. the platform's debate module features a forum-like structure where a discussion topic is defined and displayed at the top of the page, enabling users to comment on the topic or respond to other participants' comments. additional details about the platform's features are available on the adhocracy+ website foot_3 . we extend adhocracy+ by importing the ai tools into the debate module, as shown in figure 2 (left). a more detailed view is shown in figure 2 in our experiments, we ran the ai tools locally on a linux server. but the ai tools could also be run as services where communication is handled via rest api. ## evaluation in the following, we analyze the effectiveness of our two proposed modules. we start by evaluating both models on existing datasets and measure how well they perform in terms of accuracy and f1 score. furthermore, we conducted a large-scale user study to evaluate participants' satisfaction when using the modules in a real online discussion as well as to gauge the effects of the modules on other perceptions and behaviors of the participants. ## conclusion in this work we present extensions to the adhoc-racy+ platform for citizen participation. we implemented two additional modules to support more deliberative online discussions. in the comment recommendation module participants are confronted with opposing views to encourage user interaction, hence improving the reciprocity in the discussion. the deliberative quality module aims to improve the quality of contributed comments by automatically highlighting the most deliberative ones. in a large-scale user study, we tested the effects of both ai modules. we found that the comment recommendation module increased participation on the platform and improved users' perception of the deliberative quality of the discussions while not diminishing their sense of autonomy. the deliberative quality module, in contrast, did not significantly improve users' perceptions of the platform or the discussions. still, there are indications that both modules had a positive influence on the discussions, albeit independently of whether ai was involved or not. we see great potential in the features we presented to support human actors in conducting large online discussions. certainly it remains an open task to improve the ai to a level, where people perceive its selection performance as far superior than random selection. the resulting platform is freely available under an open source license and can hopefully be used for political decision-making in the future. future work. in the future we want to examine how both ai extensions to adhocracy+ can be further improved. this means gathering and annotating additional conversational data to fine-tune and improve both models. to further evaluate effects of both modules on the comment quality within the discussions, content analyses are currently being carried out.