mlsanityData to 7 Oct 2026

A Large Self-Annotated Corpus for Sarcasm

arXiv 1704.05579 ↗First posted 19 Apr 2017cs.CL cs.AI cs.LGcited by 63

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Abstract (arXiv)

We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements -- 10 times more than any previous dataset -- and many times more instances of non-sarcastic statements, allowing for learning in both balanced and unbalanced label regimes. Each statement is furthermore self-annotated -- sarcasm is labeled by the author, not an independent annotator -- and provided with user, topic, and conversation context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm detection, and evaluate baseline methods.

Key facts. Cited by 63 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2017.

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Citing papers per year: 2021: 11; 2022: 9; 2023: 12; 2024: 10; 2025: 15; 2026: 6.

63 papers.

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  1. 2025BIG-Bench Extra Hardcited by 123
  2. 2023Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmarkcited by 62
  3. 2021Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactionscited by 35
  4. 2022A Multimodal Corpus for Emotion Recognition in Sarcasmcited by 29
  5. 2023Human-Aligned Calibration for AI-Assisted Decision Makingcited by 28
  6. 2022Uncalibrated Models Can Improve Human-AI Collaborationcited by 26
  7. 2024A Survey of Multimodal Sarcasm Detectioncited by 19
  8. 2021A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflictcited by 14
  9. 2022Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approachescited by 12
  10. 2023Source Attribution for Large Language Model-Generated Datacited by 11
  11. 2024Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligencecited by 11
  12. 2024SoMeLVLM: A Large Vision Language Model for Social Media Processingcited by 11
38 more
  1. 2025Large Language Models for Subjective Language Understanding: A Surveycited by 9
  2. 2021Reddit Entity Linking Datasetcited by 7
  3. 2023Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detectioncited by 7
  4. 2023On Sarcasm Detection with OpenAI GPT-based Modelscited by 7
  5. 2023DocMSU: A Comprehensive Benchmark for Document-level Multimodal Sarcasm Understandingcited by 7
  6. 2021Latent-Optimized Adversarial Neural Transfer for Sarcasm Detectioncited by 6
  7. 2021Sarcasm Detection: A Comparative Studycited by 6
  8. 2024Systematic Literature Review: Computational Approaches for Humour Style Classificationcited by 6
  9. 2024Generalizable Sarcasm Detection Is Just Around The Corner, Of Course!cited by 5
  10. 2024BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of Englishcited by 5
  11. 2022Cyclical Curriculum Learningcited by 4
  12. 2023Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understandingcited by 4
  13. 2025ContextLeak: Auditing Leakage in Private In-Context Learning Methodscited by 4
  14. 2022A Survey on Automated Sarcasm Detection on Twittercited by 3
  15. 2022ConFiguRe: Exploring Discourse-level Chinese Figures of Speechcited by 3
  16. 2023An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detectioncited by 3
  17. 2025Perspective Transition of Large Language Models for Solving Subjective Taskscited by 3
  18. 2021Impact of Target Word and Context on End-to-End Metonymy Detectioncited by 2
  19. 2022Computational Sarcasm Analysis on Social Media: A Systematic Reviewcited by 2
  20. 2022Sarcasm Detection Framework Using Context, Emotion and Sentiment Featurescited by 2
  21. 2023Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learningcited by 2
  22. 2024KoCoSa: Korean Context-aware Sarcasm Detection Datasetcited by 2
  23. 2021MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Trainingcited by 1
  24. 2021Parallel Deep Learning-Driven Sarcasm Detection from Pop Culture Text and English Humor Literaturecited by 1
  25. 2023"When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruitycited by 1
  26. 2023Sarcasm Detection in a Disaster Contextcited by 1
  27. 2024Emotional RAG LLMs: Reading Comprehension for the Open Internetcited by 1
  28. 2025Evaluating Large Language Models Against Human Annotators in Latent Content Analysis: Sentiment, Political Leaning, Emotional Intensity, and Sarcasmcited by 1
  29. 2025Intermediate-Task Transfer Learning: Leveraging Sarcasm Detection for Stance Detectioncited by 1
  30. 2025Sarcasm Detection as a Catalyst: Improving Stance Detection with Cross-Target Capabilitiescited by 1
  31. 2025A Transformer and Prototype-based Interpretable Model for Contextual Sarcasm Detectioncited by 1
  32. 2026Emotion is Not Just a Label: Latent Emotional Factors in LLM Processingcited by 1
  33. 2026ProSarc: Prosody-Aware Sarcasm Recognition Framework via Temporal Prosodic Incongruitycited by 1
  34. 2021Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social mediacited by 0
  35. 2021Integrating extracted information from bert and multiple embedding methods with the deep neural network for humour detectioncited by 0
  36. 2021SASICM A Multi-Task Benchmark For Subtext Recognitioncited by 0
  37. 2022Finetuning for Sarcasm Detection with a Pruned Datasetcited by 0
  38. 2023Researchers eye-view of sarcasm detection in social media textual contentcited by 0

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