@inproceedings{behrendt-etal-2024-aqua,
    title = "{AQ}u{A} {--} Combining Experts{'} and Non-Experts{'} Views To Assess Deliberation Quality in Online Discussions Using {LLM}s",
    author = "Behrendt, Maike  and
      Wagner, Stefan Sylvius  and
      Ziegele, Marc  and
      Wilms, Lena  and
      Stoll, Anke  and
      Heinbach, Dominique  and
      Harmeling, Stefan",
    editor = "Hautli-Janisz, Annette  and
      Lapesa, Gabriella  and
      Anastasiou, Lucas  and
      Gold, Valentin  and
      Liddo, Anna De  and
      Reed, Chris",
    booktitle = "Proceedings of the First Workshop on Language-driven Deliberation Technology (DELITE) @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.delite-1.1",
    pages = "1--12",
    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.",
}

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.
Behrendt, Maike Wagner, Stefan Sylvius Ziegele, Marc Wilms, Lena Stoll, Anke Heinbach, Dominique Harmeling, Stefan
AQuA – Combining Experts' and Non-Experts' Views To Assess Deliberation Quality in Online Discussions Using LLMs