title: Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging
    Fabricated Claims with Humorous Content
summary: 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.
paper_authors._id: 67dcc5e41f94b594ef4c0312
paper_authors.name: Sai Kartheek Reddy Kasu
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-21T01:51:18.039Z
paper_authors._id: 651692d718f3a57f869a5a0a
paper_authors.avatarUrl: /avatars/d5fe48de11e46675b05e1e2e1cf3505c.svg
paper_authors.fullname: Sai Kartheek Reddy
paper_authors.type: user
paper_authors.user: UVSKKR
paper_authors._id: 67dcc5e41f94b594ef4c0313
paper_authors.name: Shankar Biradar
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T13:11:38.661Z
paper_authors._id: 6443c91b79e7797ce719dbaf
paper_authors.avatarUrl: /avatars/5c981c39075b35cc0ea69bf0253829a0.svg
paper_authors.fullname: shankar Biradar
paper_authors.type: user
paper_authors.user: shankarB
paper_authors._id: 67dcc5e41f94b594ef4c0314
paper_authors.name: Sunil Saumya
paper_title: Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging
    Fabricated Claims with Humorous Content
paper_summary: 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.
paper_ai_summary: The Deceptive Humor Dataset, a multilingual benchmark, provides a resource for studying humor in fabricated claims and misinformation, with classifications for satire levels and humor categories, facilitating the analysis of how humor interacts with deceptive content.
paper_ai_keywords: Deceptive Humor Dataset ChatGPT-4o Satire Level Humor Categories Dark Humor Irony Social Commentary Wordplay Absurdity multilingual benchmark deceptive humor detection models