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.

Exchanging arguments is an important part in communication, but we are often flooded with lots of arguments for different positions or are captured in filter bubbles. Tools which can present strong arguments relevant to oneself could help to reduce those problems. To be able to evaluate algorithms which can predict how convincing an argument is, we have collected a dataset with more than 900 arguments and personal attitudes of 600 individuals, which we present in this paper. Based on this data, we suggest three recommender tasks, for which we provide two baseline results from a simple majority classifier and a more complex nearest-neighbor algorithm. Our results suggest that better algorithms can still be developed, and we invite the community to improve on our results.