# Explaining Humour Style Classifications: An XAI Approach to Understanding Computational Humour Analysis

arXiv: https://arxiv.org/abs/2501.02891 · HTML: https://mlsanity.com/paper/2501.02891/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 6 Jan 2025.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Towards Faithful Model Explanation in NLP: A Survey](https://mlsanity.com/paper/2209.11326/) (2022)
- [ColBERT: Using BERT Sentence Embedding in Parallel Neural Networks for Computational Humor](https://mlsanity.com/paper/2004.12765/) (2020)
- [Systematic Literature Review: Computational Approaches for Humour Style Classification](https://mlsanity.com/paper/2402.01759/) (2024)
- [A Two-Model Approach for Humour Style Recognition](https://mlsanity.com/paper/2410.12842/) (2024)
- [THInC: A Theory-Driven Framework for Computational Humor Detection](https://mlsanity.com/paper/2409.01232/) (2024)

## Built on by (most cited first, 2 of 2)

- [Mitigating Strong-Modality Collapse in Multimodal Learning via Inverted Asymmetric Fusion](https://mlsanity.com/paper/2608.26879/) (2026)
- [MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions](https://mlsanity.com/paper/2609.11322/) (2026)

## Abstract (arXiv)

Humour styles can have either a negative or a positive impact on well-being. Given the importance of these styles to mental health, significant research has been conducted on their automatic identification. However, the automated machine learning models used for this purpose are black boxes, making their prediction decisions opaque. Clarity and transparency are vital in the field of mental health. This paper presents an explainable AI (XAI) framework for understanding humour style classification, building upon previous work in computational humour analysis. Using the best-performing single model (ALI+XGBoost) from prior research, we apply comprehensive XAI techniques to analyse how linguistic, emotional, and semantic features contribute to humour style classification decisions. Our analysis reveals distinct patterns in how different humour styles are characterised and misclassified, with particular emphasis on the challenges in distinguishing affiliative humour from other styles. Through detailed examination of feature importance, error patterns, and misclassification cases, we identify key factors influencing model decisions, including emotional ambiguity, context misinterpretation, and target identification. The framework demonstrates significant utility in understanding model behaviour, achieving interpretable insights into the complex interplay of features that define different humour styles. Our findings contribute to both the theoretical understanding of computational humour analysis and practical applications in mental health, content moderation, and digital humanities research.