# The Computational Anatomy of Humility: Modeling Intellectual Humility in Online Public Discourse

arXiv: https://arxiv.org/abs/2410.15182 · HTML: https://mlsanity.com/paper/2410.15182/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 41 references that are in the map.
- First posted on arXiv 19 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Primacy Effect of ChatGPT](https://mlsanity.com/paper/2310.13206/) (2023)
- [Me, My Echo Chamber, and I: Introspection on Social Media Polarization](https://mlsanity.com/paper/1803.01731/) (2018)
- [Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content](https://mlsanity.com/paper/2112.00773/) (2021)
- [Perspective-taking to Reduce Affective Polarization on Social Media](https://mlsanity.com/paper/2110.05596/) (2021)
- [Engaging Politically Diverse Audiences on Social Media](https://mlsanity.com/paper/2111.02646/) (2021)

## Built on by (most cited first, 3 of 3)

- ["My life is miserable, have to sign 500 autographs everyday": Exposing Humblebragging, the Brags in Disguise](https://mlsanity.com/paper/2412.20057/) (2024)
- [Advancing Uto-Aztecan Language Technologies: A Case Study on the Endangered Comanche Language](https://mlsanity.com/paper/2505.18159/) (2025)
- [What Software Engineering Looks Like to AI Agents? -- An Empirical Study of AI-Only Technical Discourse on MoltBook](https://mlsanity.com/paper/2605.08380/) (2026)

## Abstract (arXiv)

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 automating this measurement. Our best model achieves a Macro-F1 score of 0.64 across labels (and 0.70 when predicting IH/IA/Neutral at the coarse level), higher than an expected naive baseline of 0.51 (0.32 for IH/IA/Neutral) but lower than a human annotator-informed upper bound of 0.85 (0.83 for IH/IA/Neutral). Our results both highlight the challenging nature of detecting IH online -- opening the door to new directions in NLP research -- and also lay a foundation for computational social science researchers interested in analyzing and fostering more IH in online public discourse.