# A Survey of Multimodal Sarcasm Detection

arXiv: https://arxiv.org/abs/2410.18882 · HTML: https://mlsanity.com/paper/2410.18882/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://mlsanity.com/paper/1810.02508/) (2018)
- [Fine-tuned CLIP Models are Efficient Video Learners](https://mlsanity.com/paper/2212.03640/) (2022)
- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse](https://mlsanity.com/paper/2401.01523/) (2024)
- [Detecting Sarcasm in Multimodal Social Platforms](https://mlsanity.com/paper/1608.02289/) (2016)
- [iSarcasm: A Dataset of Intended Sarcasm](https://mlsanity.com/paper/1911.03123/) (2019)
- [Multimodal Learning using Optimal Transport for Sarcasm and Humor Detection](https://mlsanity.com/paper/2110.10949/) (2021)
- [MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System](https://mlsanity.com/paper/2307.07135/) (2023)
- [A Multimodal Corpus for Emotion Recognition in Sarcasm](https://mlsanity.com/paper/2206.02119/) (2022)
- [Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancement](https://mlsanity.com/paper/2210.03501/) (2022)
- [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://mlsanity.com/paper/1805.06413/) (2018)
- [Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanation](https://mlsanity.com/paper/2112.04873/) (2021)
- [MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Tasks](https://mlsanity.com/paper/2310.09036/) (2023)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://mlsanity.com/paper/2109.03587/) (2021)
- [!Qu'e maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baseline](https://mlsanity.com/paper/2105.05542/) (2021)
- [Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection](https://mlsanity.com/paper/2310.01430/) (2023)

## Built on by (most cited first, 19 of 19)

- [Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routing](https://mlsanity.com/paper/2509.15361/) (2025)
- [MUStReason: A Benchmark for Diagnosing Pragmatic Reasoning in Video-LMs for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.23727/) (2025)
- [Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Models](https://mlsanity.com/paper/2503.12149/) (2025)
- [Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.11852/) (2025)
- [What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels](https://mlsanity.com/paper/2512.16832/) (2025)
- [Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech](https://mlsanity.com/paper/2502.09004/) (2025)
- [CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark](https://mlsanity.com/paper/2604.16372/) (2026)
- [MuSaG: A Multimodal German Sarcasm Dataset with Full-Modal Annotations](https://mlsanity.com/paper/2510.24178/) (2025)
- [GDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2601.20618/) (2026)
- [SocialNLI: A Dialogue-Centric Social Inference Dataset](https://mlsanity.com/paper/2510.05458/) (2025)
- [Sarcasm Detection on Reddit Using Classical Machine Learning and Feature Engineering](https://mlsanity.com/paper/2512.04396/) (2025)
- [Exploring the Performance of Large Language Models on Subjective Span Identification Tasks](https://mlsanity.com/paper/2601.00736/) (2026)
- [Taiwan Safety Benchmark and Breeze Guard: Toward Trustworthy AI for Taiwanese Mandarin](https://mlsanity.com/paper/2603.07286/) (2026)
- [MUNIChus: Multilingual News Image Captioning Benchmark](https://mlsanity.com/paper/2603.10613/) (2026)
- [PC-MNet: Dual-Level Congruity Modeling for Multimodal Sarcasm Detection via Polarity-Modulated Attention](https://mlsanity.com/paper/2605.02447/) (2026)
- [Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges](https://mlsanity.com/paper/2607.19011/) (2026)
- [Reading Between the Frames of Multimodal Drivelology](https://mlsanity.com/paper/2608.04939/) (2026)
- [MoCA: Implicit Social Context Analysis](https://mlsanity.com/paper/2608.05825/) (2026)
- [MexHat: A Dataset for Hate Speech Detection in Mexican Spanish Videos](https://mlsanity.com/paper/2609.31553/) (2026)

## Abstract (arXiv)

Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm detection have been carried out on text only, sarcasm detection often requires additional information present in tonality, facial expression, and contextual images. This has led to the introduction of multimodal models, opening the possibility to detect sarcasm in multiple modalities such as audio, images, text, and video. In this paper, we present the first comprehensive survey on multimodal sarcasm detection - henceforth MSD - to date. We survey papers published between 2018 and 2023 on the topic, and discuss the models and datasets used for this task. We also present future research directions in MSD.