AQuA -- Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs

TL;DR

AQuA is a novel additive scoring method that combines multiple deliberative indices to assess online discussion quality, improving transparency and aligning well with expert and non-expert annotations.

Contribution

Introduces AQuA, a comprehensive and transparent deliberative quality score using adapter models for multiple indices, bridging expert and non-expert assessments.

Findings

AQuA correlates strongly with expert annotations.

AQuA aligns well with non-expert perceived deliberativeness.

The method is easily computed from pre-trained adapters.

Abstract

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.