The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse
Xiaobo Guo, Neil Potnis, Melody Yu, Nabeel Gillani, Soroush Vosoughi

TL;DR
This paper develops computational methods to measure intellectual humility in online discourse, using large language models trained on manually annotated data, aiming to promote healthier, more constructive online discussions.
Contribution
It introduces a novel approach to automatically detect intellectual humility in social media posts, combining manual annotation with LLM-based models, advancing NLP tools for social science research.
Findings
Best model achieves Macro-F1 of 0.64 for IH detection
Model outperforms naive baseline but below human upper bound
Highlights challenges and opportunities in automating IH measurement
Abstract
The ability for individuals to constructively engage with one another across lines of difference is a critical feature of a healthy pluralistic society. This is also true in online discussion spaces like social media platforms. To date, much social media research has focused on preventing ills -- like political polarization and the spread of misinformation. While this is important, enhancing the quality of online public discourse requires not just reducing ills but also promoting foundational human virtues. In this study, we focus on one particular virtue: ``intellectual humility'' (IH), or acknowledging the potential limitations in one's own beliefs. Specifically, we explore the development of computational methods for measuring IH at scale. We manually curate and validate an IH codebook on 350 posts about religion drawn from subreddits and use them to develop LLM-based models for…
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Code & Models
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Taxonomy
TopicsMisinformation and Its Impacts
MethodsFocus
