# Automatic Sarcasm Detection: A Survey

arXiv: https://arxiv.org/abs/1602.03426 · HTML: https://mlsanity.com/paper/1602.03426/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2016.

## Built on by (most cited first, 21 of 21)

- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [When did you become so smart, oh wise one?! Sarcasm Explanation in Multi-modal Multi-party Dialogues](https://mlsanity.com/paper/2203.06419/) (2022)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://mlsanity.com/paper/2109.03587/) (2021)
- [Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection](https://mlsanity.com/paper/2310.01430/) (2023)
- [Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues](https://mlsanity.com/paper/2211.11049/) (2022)
- [AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets](https://mlsanity.com/paper/2103.01679/) (2021)
- [News Headlines Dataset For Sarcasm Detection](https://mlsanity.com/paper/2212.06035/) (2022)
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- [Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.11852/) (2025)
- [Interpretable Bangla Sarcasm Detection using BERT and Explainable AI](https://mlsanity.com/paper/2303.12772/) (2023)
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- [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)
- [sarcasm detection and quantification in arabic tweets](https://mlsanity.com/paper/2108.01425/) (2021)
- [Why is "Problems" Predictive of Positive Sentiment? A Case Study of Explaining Unintuitive Features in Sentiment Classification](https://mlsanity.com/paper/2406.03594/) (2024)
- [SASICM A Multi-Task Benchmark For Subtext Recognition](https://mlsanity.com/paper/2106.06944/) (2021)
- [Sentiment Analysis and Sarcasm Detection of Indian General Election Tweets](https://mlsanity.com/paper/2201.02127/) (2022)
- [Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges](https://mlsanity.com/paper/2406.16674/) (2024)
- [PromptAug: Fine-grained Conflict Classification Using Data Augmentation](https://mlsanity.com/paper/2506.22491/) (2025)
- [Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies](https://mlsanity.com/paper/2607.00968/) (2026)

## Abstract (arXiv)

Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, sarcasm detection has witnessed great interest from the sentiment analysis community. This paper is the first known compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and use of context beyond target text. In this paper, we describe datasets, approaches, trends and issues in sarcasm detection. We also discuss representative performance values, shared tasks and pointers to future work, as given in prior works. In terms of resources that could be useful for understanding state-of-the-art, the survey presents several useful illustrations - most prominently, a table that summarizes past papers along different dimensions such as features, annotation techniques, data forms, etc.