Deceptive Humor: A Synthetic Multilingual Benchmark Dataset for Bridging Fabricated Claims with Humorous Content
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Abstract (arXiv)
In the evolving landscape of online discourse, misinformation increasingly adopts humorous tones to evade detection and gain traction. This work introduces Deceptive Humor as a novel research direction, emphasizing how false narratives, when coated in humor, can become more difficult to detect and more likely to spread. To support research in this space, we present the Deceptive Humor Dataset (DHD) a collection of humor-infused comments derived from fabricated claims using the ChatGPT-4o model. Each entry is labeled with a Satire Level (from 1 for subtle satire to 3 for overt satire) and categorized into five humor types: Dark Humor, Irony, Social Commentary, Wordplay, and Absurdity. The dataset spans English, Telugu, Hindi, Kannada, Tamil, and their code-mixed forms, making it a valuable resource for multilingual analysis. DHD offers a structured foundation for understanding how humor can serve as a vehicle for the propagation of misinformation, subtly enhancing its reach and impact. Strong baselines are established to encourage further research and model development in this emerging area.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 20 Mar 2025.
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22 of 39 references.
References · 22
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
- 2020DeBERTa: Decoding-enhanced BERT with Disentangled Attentioncited by 2,181
10 more
- 2018FEVER: a large-scale dataset for Fact Extraction and VERificationcited by 1,375
- 2020Multilingual Denoising Pre-training for Neural Machine Translationcited by 1,035
- 2024Phi-4 Technical Reportcited by 711
- 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 363
- 2024Best Practices and Lessons Learned on Synthetic Datacited by 106
- 2020SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!cited by 78
- 2021Generate, Annotate, and Learn: NLP with Synthetic Textcited by 42
- 2020Hostility Detection Dataset in Hindicited by 31
- 2019"President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlinescited by 27
- 2024Blind Spots and Biases: Exploring the Role of Annotator Cognitive Biases in NLPcited by 11
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.