Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging
  Fabricated Claims with Humorous Content
Authors: name: Sai Kartheek Reddy Kasu, name: Shankar Biradar, name: Sunil Saumya

This paper presents the Deceptive Humor Dataset (DHD), a novel resource for
studying humor derived from fabricated claims and misinformation. In an era of
rampant misinformation, understanding how humor intertwines with deception is
essential. DHD consists of humor-infused comments generated from false
narratives, incorporating fabricated claims and manipulated information using
the ChatGPT-4o model. Each instance is labeled with a Satire Level, ranging
from 1 for subtle satire to 3 for high-level satire and classified into five
distinct Humor Categories: Dark Humor, Irony, Social Commentary, Wordplay, and
Absurdity. The dataset spans multiple languages including English, Telugu,
Hindi, Kannada, Tamil, and their code-mixed variants (Te-En, Hi-En, Ka-En,
Ta-En), making it a valuable multilingual benchmark. By introducing DHD, we
establish a structured foundation for analyzing humor in deceptive contexts,
paving the way for a new research direction that explores how humor not only
interacts with misinformation but also influences its perception and spread. We
establish strong baselines for the proposed dataset, providing a foundation for
future research to benchmark and advance deceptive humor detection models.