mlsanityData to 14 Oct 2026

A Survey of Multimodal Sarcasm Detection

arXiv 2410.18882 ↗First posted 24 Oct 2024cs.CLcited by 19

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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.

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

23 of 53 references.

References · 23

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  4. 2019LXMERT: Learning Cross-Modality Encoder Representations from Transformerscited by 1,553
  5. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,312
  6. 2019VisualBERT: A Simple and Performant Baseline for Vision and Languagecited by 1,296
  7. 2018MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversationscited by 643
  8. 2022Fine-tuned CLIP Models are Efficient Video Learnerscited by 179
  9. 2019Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)cited by 174
  10. 2017A Large Self-Annotated Corpus for Sarcasmcited by 64
  11. 2024GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abusecited by 52
  12. 2016Detecting Sarcasm in Multimodal Social Platformscited by 44
11 more
  1. 2019iSarcasm: A Dataset of Intended Sarcasmcited by 41
  2. 2021Multimodal Learning using Optimal Transport for Sarcasm and Humor Detectioncited by 37
  3. 2023MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection Systemcited by 31
  4. 2022A Multimodal Corpus for Emotion Recognition in Sarcasmcited by 29
  5. 2022Towards Multi-Modal Sarcasm Detection via Hierarchical Congruity Modeling with Knowledge Enhancementcited by 27
  6. 2018CASCADE: Contextual Sarcasm Detection in Online Discussion Forumscited by 26
  7. 2021Nice perfume. How long did you marinate in it? Multimodal Sarcasm Explanationcited by 25
  8. 2023MM-BigBench: Evaluating Multimodal Models on Multimodal Content Comprehension Taskscited by 18
  9. 2021A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflictcited by 14
  10. 2021!Qu'e maravilla! Multimodal Sarcasm Detection in Spanish: a Dataset and a Baselinecited by 8
  11. 2023Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detectioncited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 9; 2026: 10.

19 papers.

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  1. 2025Beyond Spurious Signals: Debiasing Multimodal Large Language Models via Counterfactual Inference and Adaptive Expert Routingcited by 5
  2. 2025MUStReason: A Benchmark for Diagnosing Pragmatic Reasoning in Video-LMs for Multimodal Sarcasm Detectioncited by 5
  3. 2025Seeing Sarcasm Through Different Eyes: Analyzing Multimodal Sarcasm Perception in Large Vision-Language Modelscited by 4
  4. 2025Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detectioncited by 3
  5. 2025What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channelscited by 3
  6. 2025Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speechcited by 2
  7. 2026CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmarkcited by 2
  8. 2025MuSaG: A Multimodal German Sarcasm Dataset with Full-Modal Annotationscited by 1
  9. 2026GDCNet: Generative Discrepancy Comparison Network for Multimodal Sarcasm Detectioncited by 1
  10. 2025SocialNLI: A Dialogue-Centric Social Inference Datasetcited by 0
  11. 2025Sarcasm Detection on Reddit Using Classical Machine Learning and Feature Engineeringcited by 0
  12. 2026Exploring the Performance of Large Language Models on Subjective Span Identification Taskscited by 0
7 more
  1. 2026Taiwan Safety Benchmark and Breeze Guard: Toward Trustworthy AI for Taiwanese Mandarincited by 0
  2. 2026MUNIChus: Multilingual News Image Captioning Benchmarkcited by 0
  3. 2026PC-MNet: Dual-Level Congruity Modeling for Multimodal Sarcasm Detection via Polarity-Modulated Attentioncited by 0
  4. 2026Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challengescited by 0
  5. 2026Reading Between the Frames of Multimodal Drivelologycited by 0
  6. 2026MoCA: Implicit Social Context Analysiscited by 0
  7. 2026MexHat: A Dataset for Hate Speech Detection in Mexican Spanish Videoscited by 0