# iSarcasm: A Dataset of Intended Sarcasm

arXiv: https://arxiv.org/abs/1911.03123 · HTML: https://mlsanity.com/paper/1911.03123/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2019.

## Built on by (most cited first, 30 of 41)

- [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://mlsanity.com/paper/2401.03729/) (2024)
- [Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science](https://mlsanity.com/paper/2305.15041/) (2023)
- [A Multimodal Corpus for Emotion Recognition in Sarcasm](https://mlsanity.com/paper/2206.02119/) (2022)
- [Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation](https://mlsanity.com/paper/2112.04873/) (2021)
- [On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations](https://mlsanity.com/paper/2204.12708/) (2022)
- [A Survey of Multimodal Sarcasm Detection](https://mlsanity.com/paper/2410.18882/) (2024)
- [Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language](https://mlsanity.com/paper/2103.01242/) (2021)
- [Automatic Identification and Classification of Bragging in Social Media](https://mlsanity.com/paper/2203.05840/) (2022)
- [Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future](https://mlsanity.com/paper/2403.14659/) (2024)
- [Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://mlsanity.com/paper/2104.09261/) (2021)
- [Combining Humor and Sarcasm for Improving Political Parody Detection](https://mlsanity.com/paper/2205.03313/) (2022)
- [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://mlsanity.com/paper/2209.04712/) (2022)
- [Systematic Literature Review: Computational Approaches for Humour Style Classification](https://mlsanity.com/paper/2402.01759/) (2024)
- [UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation](https://mlsanity.com/paper/2204.08198/) (2022)
- [Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!](https://mlsanity.com/paper/2404.06357/) (2024)
- [BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English](https://mlsanity.com/paper/2412.04726/) (2024)
- [Can Third-parties Read Our Emotions?](https://mlsanity.com/paper/2504.18673/) (2025)
- [SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm](https://mlsanity.com/paper/2301.02521/) (2023)
- [X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection](https://mlsanity.com/paper/2211.16883/) (2022)
- [KoCoSa: Korean Context-aware Sarcasm Detection Dataset](https://mlsanity.com/paper/2402.14428/) (2024)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- [Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection](https://mlsanity.com/paper/2511.21066/) (2025)
- [Detecting Multidimensional Political Incivility on Social Media](https://mlsanity.com/paper/2305.14964/) (2023)
- [Sarcasm Detection in a Disaster Context](https://mlsanity.com/paper/2308.08156/) (2023)
- [InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks](https://mlsanity.com/paper/2402.14702/) (2024)
- [Emotional RAG LLMs: Reading Comprehension for the Open Internet](https://mlsanity.com/paper/2408.11189/) (2024)
- [Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasm](https://mlsanity.com/paper/2501.02532/) (2025)
- [Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word Embeddings](https://mlsanity.com/paper/2108.09924/) (2021)
- [Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation](https://mlsanity.com/paper/2203.04111/) (2022)
- [CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic](https://mlsanity.com/paper/2206.08415/) (2022)

## Abstract (arXiv)

We consider the distinction between intended and perceived sarcasm in the context of textual sarcasm detection. The former occurs when an utterance is sarcastic from the perspective of its author, while the latter occurs when the utterance is interpreted as sarcastic by the audience. We show the limitations of previous labelling methods in capturing intended sarcasm and introduce the iSarcasm dataset of tweets labeled for sarcasm directly by their authors. Examining the state-of-the-art sarcasm detection models on our dataset showed low performance compared to previously studied datasets, which indicates that these datasets might be biased or obvious and sarcasm could be a phenomenon under-studied computationally thus far. By providing the iSarcasm dataset, we aim to encourage future NLP research to develop methods for detecting sarcasm in text as intended by the authors of the text, not as labeled under assumptions that we demonstrate to be sub-optimal.