Online discussions can be rough, especially when it comes to political issues. They are often characterized by a harsh tone which discourages many people from participating in them at all. At the same time, these discussions are very important for democracy as they promote exchange and help individuals form their own opinions. While Artificial Intelligence (AI) may be detrimental to the quality of discussions (e.g. when used in spam bots), it also offers a promising opportunity to support constructive and inclusive discussions, for example by making them more civil. To strengthen such discursive integration we have engaged in a co-creation process with non-academic stakeholders to develop a discussion assistant prototype that i) identifies likely problematic comments for a possible rephrasing and ii) offers authors help with reformulation by letting generative AI suggest improvements like more civil wording. In this paper, we describe the process of co-creative research and the current status of the discussion assistant, which is still being developed and improved.

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.

Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science. Research has identified various indicators to assess online discussion quality, and with deep learning advancements, automating these measures has become feasible. While some studies focus on analyzing specific quality indicators, a comprehensive quality score incorporating various deliberative aspects is often preferred. In this work, we introduce AQuA, an additive score that calculates a unified deliberative quality score from multiple indices for each discussion post. Unlike other singular scores, AQuA preserves information on the deliberative aspects present in comments, enhancing model transparency. We develop adapter models for 20 deliberative indices, and calculate correlation coefficients between experts’ annotations and the perceived deliberativeness by non-experts to weigh the individual indices into a single deliberative score. We demonstrate that the AQuA score can be computed easily from pre-trained adapters and aligns well with annotations on other datasets that have not be seen during training. The analysis of experts’ vs. non-experts’ annotations confirms theoretical findings in the social science literature.