Why Do We Laugh? Annotation and Taxonomy Generation for Laughable Contexts in Spontaneous Text Conversation (2025)

Abstract: Laughter serves as a multifaceted communicative signal in human interaction, yet its identification within dialogue presents a significant challenge for conversational AI systems. This study addresses this challenge by annotating laughable contexts in Japanese spontaneous text conversation data and developing a taxonomy to classify the underlying reasons for such contexts. Initially, multiple annotators manually labeled laughable contexts using a binary decision (laughable or non-laughable). Subsequently, an LLM was used to generate explanations for the binary annotations of laughable contexts, which were then categorized into a taxonomy comprising ten categories, including “Empathy and Affinity” and “Humor and Surprise,” highlighting the diverse range of laughter-inducing scenarios. The study also evaluated GPT-4o’s performance in recognizing the majority labels of laughable contexts, achieving an F1 score of 43.14%. These findings contribute to the advancement of conversational AI by establishing a foundation for more nuanced recognition and generation of laughter, ultimately fostering more natural and engaging human-AI interactions.

## introduction in human dialogue, laughter serves as a communicative signal conveying humor, empathy, surprise, or social bonding @xcite @xcite . however, its mechanisms are complex and multifaceted, and understanding them remains a long-term challenge for dialogue systems aiming to achieve human-like interaction @xcite @xcite @xcite . furthermore, traditional approaches to modeling laughter and humor have often been limited to scenarios involving explicit auditory or visual stimuli, with few addressing the subtle contextual nuances present in sponta- i think that's a wonderful attitude. i always end up talking about myself, so i should follow your example. no b: is that so? but does your husband listen to your stories? no a: yes, yes, he listens to me. i wonder if i'm putting too much on him? no b: i don't think so! he's so kind. my husband doesn't seem to listen to me. huh, that's strange. ## annotation of laughable context we annotated laughable contexts in the realper-sonachat dataset @xcite . this textual data contains one-on-one japanese spontaneous conversation where participants chat without assuming assigned personas. it includes approximately 30 utterances per conversation, totaling around 14,000 dialogues. we annotated 900 dialogues, with plans to annotate the remainder in future work. during the annotation process, each annotator reviewed each dialogue and, after the initial two greeting utterances, made a binary decision for whether the next person would laugh (laughable) or not. five annotators assigned these binary labels to each utterance. ## generating taxonomy of laughable reason towards developing human-like laughter behaviors in llms, we investigated the reasons behind human annotators' recognition of laughable contexts. in this section, we used only samples with majority labels marked as laughable (3,739 samples). since manual annotations are costly, we utilized gpt-4o to generate explanations for the human judgments. for example, a generated reason for the example context in if we were to speculate on the reasons a third party might judge that person a laughed at person b's final remark in this conversation, the following points can be considered: element of humor: person b's comment, "my husband doesn't seem to listen to me. huh, that's strange.," contains a touch of self-deprecating humor. this lighthearted tone, making fun of their own situation, can be amusing to the listener. (...) relaxed atmosphere: the overall tone of the conversation seems light and relaxed, and person b's comment might have been perceived as a playful joke in line with this mood. a combination of these factors may have led the third party to interpret that person a laughed in response to person b's remark. we then aimed to summarize the generated reasoning texts for laughable contexts by applying a taxonomy generation approach using llms @xcite . first, we randomly divided the generated reason samples into smaller subsets, each comprising roughly 5% of the samples. starting with the first subset, we gave the reason sentences gpt-4o to generate initial taxonomy labels and those explanations, which we manually validated when necessary. we then iteratively refined the taxonomy by having the llm update it based on the previous taxonomy and the reason sentence data from each new subset, continuing this process until all data were processed. this resulted in ten taxonomy labels, summarized in after generating these taxonomy labels, we used the llm to assign them to each reason sample, situations where a sense of closeness and laughter is generated by sharing common experiences or emotions in a conversation. this includes empathy for shared hobbies or everyday relatable situations. 3013 (80.6%) @xcite (2) humor and surprise cases where humor or an element of surprise in the statement triggers laughter. this includes unexpected twists, wordplay, and exaggeration. 3233 (86.5%) @xcite ; martin and ford, 2018) (3) relaxed atmosphere situations where the conversation progresses in a calm, relaxed atmosphere, naturally leading to laughter. lighthearted exchanges and conversations with jokes fall into this category. 2955 (79.0%) (vettin and todt, 2004) (4) self-disclosure and friendliness situations where sharing personal stories or past mistakes creates a sense of approachability and triggers laughter. self-disclosure that makes the other person feel at ease is also included. 475 (12.7%) (gelkopf and kreitler, 1996) (5) cultural background and shared understanding laughter based on specific cultural backgrounds or shared understandings. this includes jokes related to a particular region or culture or remarks based on common superstitions or folklore. 176 (4.7%) (bryant and bainbridge, 2022; kamiloglu et al., 2022) (6) nostalgia and fondness situations where past memories or nostalgic topics trigger laughter. this includes shared past experiences and the enjoyment of recalling familiar events. 204 (5.5%) (bazzini et al., 2007) (7) self-deprecating humor situations where turning one's flaws or mistakes into humor makes the other person feel closer and triggers laughter. slightly putting oneself down can also give the other person a sense of ease. 404 (10.8%) (atkinson, 2015) (8) defying expectations situations where intentionally defying the flow of conversation or the other person's expectations creates an element of surprise and triggers laughter. this includes unexpected responses or developments. 323 (8.6%) (ginzburg et al., 2020; xu, 2022) (9) positive energy situations where positive emotions or energy in the conversation bring a smile to the other person. enjoyable topics and positive comments fall into this category. 338 (9.0%) (wang et al., 2024) (10) exaggeration situations where exaggerating things gives a comical impression and triggers laughter. exaggerated expressions can be amusing to the listener. 478 (12.8%) (mccarthy and carter, 2004) ## llm's performance on laughable context recognition we then examined how much llms, specifically gpt-4o, can recognize the laughable contexts in spontaneous text conversation. the model was tested in a zero-shot setting, instructed to first analyze the conversational context and then determine its laughability as a binary. the provided prompt included a task description for laughable context recognition, followed by a chain-of-thought (cot) reasoning approach to encourage the model to consider the reasoning behind its decision step by step. we evaluated gpt-4o's performance against the majority labels, achieving an f1 score of 43.14%, with a precision of 41.66% and recall of 44.72%. while this score was significantly above the chance level (14.8%), capturing the nuanced subtleties of conversational humor remains challenging. we then further examined the llm's performance on each generated taxonomy label. ## conclusion this study investigated laughter in the context of conversational ai by annotating laughable contexts within a japanese text dialogue dataset. a taxonomy of ten distinct reasons for laughter was generated by an llm, providing valuable insights into the multifaceted nature of laughter. subsequently, this study evaluated the ability of gpt-4o to recognize those laughable contexts. while the model's performance surpassed chance levels, it highlighted the inherent challenges in capturing the nuances of conversational humor. this automated approach employed for reasoning and taxonomy generation with llms can be applied in other scenarios where only binary (or simplified) decision labels from human annotators are available, yet more fine-grained explanations are required. future work will focus on expanding the dataset to cover other languages and cultural contexts, validating the generated taxonomy by incorporating additional linguistic research perspectives, exploring multimodal approaches, and including spoken dialogue to enhance ai's understanding of humor and social interaction.