Appropriate Incongruity Driven Human-AI Collaborative Tool to Assist Novices in Humorous Content Generation

Abstract

Creating humorous content has been shown to improve an individual's emotional well-being by decreasing stress, overcoming anxiety, and enhancing interpersonal relationships. However, it is common knowledge that a good sense of humor is not common. In this paper, we propose a natural language processing (NLP) driven collaborative tool based on appropriate incongruity theory to assist novices in writing humorous content. We use cartoon-caption writing as the use case since it is a popular method where people engage in creating humorous content. The paper describes the design of our co-authoring tool and findings from a two-part user study where (1) 20 participants used our tool to co-author cartoon captions and (2) 66 participants evaluated those captions. Our findings show that the tool helped participants to identify incongruous visual elements in the cartoon, support ideation, and expand the narrative. This resulted in co-authored captions more frequently rated funnier than those written without the tool. This approach can be appropriated to other humor generation applications including creative writing, creating memes, sketch comedy, and advertising.