THInC: A Theory-Driven Framework for Computational Humor Detection

TL;DR

This paper introduces THInC, an interpretable, theory-driven framework for computational humor detection that integrates multiple humor theories to improve classification accuracy and interpretability.

Contribution

It develops a novel ensemble of interpretable classifiers based on humor theories, bridging the gap between humor theory research and computational detection.

Findings

Achieved an F1 score of 0.85 in humor classification.

Enabled analysis of humor features aligned with theories.

Provided a foundation for automatic comparison of humor theories.

Abstract

Humor is a fundamental aspect of human communication and cognition, as it plays a crucial role in social engagement. Although theories about humor have evolved over centuries, there is still no agreement on a single, comprehensive humor theory. Likewise, computationally recognizing humor remains a significant challenge despite recent advances in large language models. Moreover, most computational approaches to detecting humor are not based on existing humor theories. This paper contributes to bridging this long-standing gap between humor theory research and computational humor detection by creating an interpretable framework for humor classification, grounded in multiple humor theories, called THInC (Theory-driven Humor Interpretation and Classification). THInC ensembles interpretable GA2M classifiers, each representing a different humor theory. We engineered a transparent flow to actively create proxy features that quantitatively reflect different aspects of theories. An implementation of this framework achieves an F1 score of 0.85. The associative interpretability of the framework enables analysis of proxy efficacy, alignment of joke features with theories, and identification of globally contributing features. This paper marks a pioneering effort in creating a humor detection framework that is informed by diverse humor theories and offers a foundation for future advancements in theory-driven humor classification. It also serves as a first step in automatically comparing humor theories in a quantitative manner.