---
title: "Who cites iSarcasm: A Dataset of Intended Sarcasm"
description: "The papers of the Elamur Research library that cite «iSarcasm: A Dataset of Intended Sarcasm», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [iSarcasm: A Dataset of Intended Sarcasm](https://md.research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/) / cited by

# Who cites «iSarcasm: A Dataset of Intended Sarcasm»

55 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/oprea-2020-isarcasm-dataset-intended-sarcasm/)

1. [A Corpus of Misunderstood Irony on Turkish Social Media](https://aclanthology.org/2026.lrec-1.879/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
2. [A Multi-Aspect Evaluation Framework for Synthetic Data: Case Study on Irony and Sarcasm](https://aclanthology.org/2026.wassa-1.23/) *— 2026 · The Proceedings for the 15th Workshop on Computational Approaches to Subjectivity, Sentiment Social Media Analysis (WASSA 2026)*
3. [CEI: A Benchmark for Evaluating Pragmatic Reasoning in Language Models](https://arxiv.org/abs/2603.09993) *— 2026 · arXiv:2603.09993*
4. [Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection](https://aclanthology.org/2026.findings-acl.1227/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
5. [IHPP: A Paragraph-Level Dataset for Investigating the Pragmatics of Hyperpartisan Italian News](https://aclanthology.org/2026.lrec-1.157/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
6. [SarcasTürk: Turkish Context-Aware Sarcasm Detection Dataset](https://aclanthology.org/2026.sigturk-1.6/) *— 2026 · Proceedings of the Second Workshop Natural Language Processing for Turkic Languages (SIGTURK 2026)*
7. [BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English](https://aclanthology.org/2025.findings-acl.441/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
8. [Can Third Parties Read Our Emotions?](https://aclanthology.org/2025.acl-long.1042/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Detecting Emotional Incongruity of Sarcasm by Commonsense Reasoning](https://aclanthology.org/2025.coling-main.608/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
10. [Donate or Create? Comparing Data Collection Strategies for Emotion-labeled Multimodal Social Media Posts](https://aclanthology.org/2025.acl-long.847/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [Glider: Global and Local Instruction-Driven Expert Router](https://aclanthology.org/2025.emnlp-main.319/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
12. [KoWit-24: A Richly Annotated Dataset of Wordplay in News Headlines](https://aclanthology.org/2025.ranlp-1.15/) *— 2025 · Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era*
13. [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://aclanthology.org/2025.findings-emnlp.1308/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
14. [Sarcasm-R1: Enhancing Sarcasm Detection through Focused Reasoning](https://aclanthology.org/2025.findings-emnlp.570/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
15. [The Difficult Case of Intended and Perceived Sarcasm: a Challenge for Humans and Large Language Models](https://aclanthology.org/2025.iwcs-main.23/) *— 2025 · Proceedings of the 16th International Conference on Computational Semantics*
16. [A Survey of Multimodal Sarcasm Detection](https://arxiv.org/abs/2410.18882) *— 2024 · arXiv:2410.18882*
17. [Generalizable Sarcasm Detection is Just Around the Corner, of Course!](https://aclanthology.org/2024.naacl-long.238/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
18. [InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks](https://aclanthology.org/2024.lrec-main.794/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
19. [KoCoSa: Korean Context-aware Sarcasm Detection Dataset](https://aclanthology.org/2024.lrec-main.864/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
20. [Sarcasm Detection in a Disaster Context](https://aclanthology.org/2024.lrec-main.1247/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
21. [Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future](https://aclanthology.org/2024.findings-acl.163/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
22. [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://aclanthology.org/2024.findings-acl.275/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
23. [EDAL: Entropy based Dynamic Attention Loss for HateSpeech Classification](https://aclanthology.org/2023.paclic-1.77/) *— 2023 · Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation*
24. [Just Like a Human Would, Direct Access to Sarcasm Augmented with Potential Result and Reaction](https://aclanthology.org/2023.acl-long.566/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [Personalized Intended and Perceived Sarcasm Detection on Twitter](https://aclanthology.org/2023.cpss-1.2/) *— 2023 · Proceedings of the 3rd Workshop on Computational Linguistics for the Political and Social Sciences*
26. [A Multimodal Corpus for Emotion Recognition in Sarcasm](https://aclanthology.org/2022.lrec-1.756/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
27. [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://md.research.elamur.ai/free/en/papers/zeng-2022-survey-automatic-irony-processing-linguistic/) *— our card · 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
28. [Amrita_CEN at SemEval-2022 Task 6: A Machine Learning Approach for Detecting Intended Sarcasm using Oversampling](https://aclanthology.org/2022.semeval-1.115/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
29. [Automatic Identification and Classification of Bragging in Social Media](https://aclanthology.org/2022.acl-long.273/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
31. [CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic](https://aclanthology.org/2022.semeval-1.117/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
32. [Combining Humor and Sarcasm for Improving Political Parody Detection](https://aclanthology.org/2022.naacl-main.131/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
33. [I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning Techniques](https://aclanthology.org/2022.semeval-1.119/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
34. [JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams](https://aclanthology.org/2022.semeval-1.145/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
35. [MarSan at SemEval-2022 Task 6: iSarcasm Detection via T5 and Sequence Learners](https://aclanthology.org/2022.semeval-1.137/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
36. [NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep Learning](https://aclanthology.org/2022.semeval-1.120/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
37. [On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations](https://aclanthology.org/2022.findings-naacl.168/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
38. [PALI-NLP at SemEval-2022 Task 6: iSarcasmEval- Fine-tuning the Pre-trained Model for Detecting Intended Sarcasm](https://aclanthology.org/2022.semeval-1.112/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
39. [Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation](https://arxiv.org/abs/2203.04111) *— 2022 · arXiv:2203.04111*
40. [Plumeria at SemEval-2022 Task 6: Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation](https://aclanthology.org/2022.semeval-1.130/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
41. [R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasm](https://aclanthology.org/2022.semeval-1.143/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
42. [SAIDS: A Novel Approach for Sentiment Analysis Informed of Dialect and Sarcasm](https://aclanthology.org/2022.wanlp-1.3/) *— 2022 · Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)*
43. [Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection](https://aclanthology.org/2022.findings-emnlp.387/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
44. [SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic](https://md.research.elamur.ai/free/en/papers/farha-2022-semeval-2022-task-6-isarcasmeval/) *— our card · 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
45. [The Effect of Arabic Dialect Familiarity on Data Annotation](https://aclanthology.org/2022.wanlp-1.39/) *— 2022 · Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)*
46. [UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation](https://aclanthology.org/2022.semeval-1.135/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
47. [Understanding the Sarcastic Nature of Emojis with SarcOji](https://aclanthology.org/2022.emoji-1.4/) *— 2022 · Proceedings of the Fifth International Workshop on Emoji Understanding and Applications in Social Media*
48. [X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection](https://aclanthology.org/2022.semeval-1.140/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
49. [stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets](https://aclanthology.org/2022.semeval-1.113/) *— 2022 · Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)*
50. [Cryptonite: A Cryptic Crossword Benchmark for Extreme Ambiguity in Language](https://aclanthology.org/2021.emnlp-main.344/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
51. [Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://aclanthology.org/2021.naacl-main.425/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
52. [Overview of the WANLP 2021 Shared Task on Sarcasm and Sentiment Detection in Arabic](https://aclanthology.org/2021.wanlp-1.36/) *— 2021 · Proceedings of the Sixth Arabic Natural Language Processing Workshop*
53. [Say `YES” to Positivity: Detecting Toxic Language in Workplace Communications](https://aclanthology.org/2021.findings-emnlp.173/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
54. [From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset](https://aclanthology.org/2020.osact-1.5/) *— 2020 · Proceedings of the 4th Workshop on Open-Source Arabic Corpora and Processing Tools, with a Shared Task on Offensive Language Detection*
55. [Reactive Supervision: A New Method for Collecting Sarcasm Data](https://aclanthology.org/2020.emnlp-main.201/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*