Automated Assessment of Deliberative Quality in Online Discussions Using Adapter Models and Crowd Annotations

Alapfogalmak

A single deliberative quality score (AQuA) is calculated by combining predictions from adapter models trained on various aspects of deliberation and insights from both expert and non-expert annotations.

Kivonat

The paper introduces AQuA, a method for automatically assessing the deliberative quality of individual comments in online discussions. The approach combines predictions from adapter models trained on 20 different deliberative aspects with insights gained from both expert and non-expert annotations of the same data.

The key highlights are:

1. 20 adapter models are trained to predict scores for different deliberative criteria, such as rationality, reciprocity, and civility.

2. Correlation coefficients between the expert annotations and non-expert perceptions of deliberativeness are calculated to determine the weights for each deliberative aspect in the final AQuA score.

3. The weighted sum of the adapter predictions is normalized to a 0-5 scale, resulting in the AQuA score, which provides a single, interpretable measure of deliberative quality.

4. Experiments show that the AQuA score aligns well with expert annotations on other datasets and can be used to automatically assess the deliberative quality of online comments.

5. The analysis of the correlation coefficients confirms findings from deliberation research, highlighting the importance of rational arguments, respectful engagement, and personal storytelling in perceived deliberative quality.

Statisztikák

The AQuA score is calculated as a weighted sum of 20 adapter predictions, where the weights are the correlation coefficients between the expert annotations and non-expert perceptions of deliberativeness.

Idézetek

"Measuring the quality of contributions in political online discussions is crucial in deliberation research and computer science."
"We argue, however, that certain aspects may be more important than others to estimate the deliberative quality of a contribution."
"Our approach combines predictions on various dimensions of deliberation with insights gained from both expert and non-expert evaluations, resulting in a single deliberative quality score."

Főbb Kivonatok

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

by Maike Behren... : arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02761.pdf

Mélyebb kérdések

How could the AQuA score be used to provide real-time feedback to users on the deliberative quality of their contributions in online discussions?

The AQuA score can be utilized to offer real-time feedback to users by integrating it into online discussion platforms. Users could receive immediate feedback on the quality of their contributions based on the deliberative aspects assessed by the AQuA score. For instance, after submitting a comment, users could receive a score indicating the level of rationality, reciprocity, civility, and other deliberative qualities present in their contribution. This feedback could be displayed alongside their comment, allowing users to reflect on and potentially revise their input to enhance its deliberative quality. Additionally, the platform could provide suggestions or tips on how to improve their score, guiding users towards more constructive and respectful interactions.

What are the potential biases or limitations of relying on crowd annotations to determine the weights for the deliberative aspects in the AQuA score?

Relying on crowd annotations to determine the weights for deliberative aspects in the AQuA score may introduce several biases and limitations. One potential bias is the subjectivity of individual crowd annotators, which can lead to inconsistencies in the assessments of deliberative quality. Different annotators may have varying interpretations of what constitutes rationality, reciprocity, or civility, resulting in discrepancies in the assigned weights. Moreover, crowd annotators may not have the expertise or background knowledge to accurately evaluate the deliberative aspects, leading to inaccuracies in the weights assigned to each criterion. Additionally, crowd annotations may be influenced by factors such as personal biases, cultural differences, or language proficiency, further impacting the reliability of the weights derived from these annotations.

How could the AQuA framework be extended to assess the deliberative quality of discussions across different languages and cultural contexts?

To extend the AQuA framework for assessing deliberative quality across different languages and cultural contexts, several steps can be taken. Firstly, the adapters used in the AQuA framework can be trained on multilingual datasets to capture the nuances of deliberative quality in diverse languages. This would involve translating comments into different languages and training adapters on each language-specific dataset. Additionally, cultural considerations can be integrated into the training process by including diverse cultural perspectives in the annotation and training data. Adapting the AQuA framework to different cultural contexts would require adjusting the weights assigned to deliberative aspects based on cultural norms and values related to communication and discourse. By incorporating multilingual and culturally diverse datasets in the training process, the AQuA framework can be extended to effectively assess deliberative quality in discussions across various languages and cultural settings.