# Detecting Sarcasm in Multimodal Social Platforms

arXiv: https://arxiv.org/abs/1608.02289 · HTML: https://mlsanity.com/paper/1608.02289/

## Key facts

- Cited by 44 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2016.

## Built on by (most cited first, 30 of 44)

- [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)
- [IRFL: Image Recognition of Figurative Language](https://mlsanity.com/paper/2303.15445/) (2023)
- [Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation](https://mlsanity.com/paper/2306.16650/) (2023)
- [A Survey of Multimodal Sarcasm Detection](https://mlsanity.com/paper/2410.18882/) (2024)
- [Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey](https://mlsanity.com/paper/2406.08068/) (2024)
- [Debiasing Multimodal Sarcasm Detection with Contrastive Learning](https://mlsanity.com/paper/2312.10493/) (2023)
- [CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models](https://mlsanity.com/paper/2405.00390/) (2024)
- [Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding](https://mlsanity.com/paper/2403.11311/) (2024)
- [A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives](https://mlsanity.com/paper/2209.04712/) (2022)
- [Sentiment-enhanced Graph-based Sarcasm Explanation in Dialogue](https://mlsanity.com/paper/2402.03658/) (2024)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)
- [InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection](https://mlsanity.com/paper/2406.16464/) (2024)
- [Multi-View Incongruity Learning for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2412.00756/) (2024)
- [RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2412.13008/) (2024)
- [FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis](https://mlsanity.com/paper/2502.16503/) (2025)
- [A Survey on Automated Sarcasm Detection on Twitter](https://mlsanity.com/paper/2202.02516/) (2022)
- [ConFiguRe: Exploring Discourse-level Chinese Figures of Speech](https://mlsanity.com/paper/2209.07678/) (2022)
- [Multi-modal Semantic Understanding with Contrastive Cross-modal Feature Alignment](https://mlsanity.com/paper/2403.06355/) (2024)
- [VyAnG-Net: A Novel Multi-Modal Sarcasm Recognition Model by Uncovering Visual, Acoustic and Glossary Features](https://mlsanity.com/paper/2408.10246/) (2024)
- [Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation Generation](https://mlsanity.com/paper/2502.07391/) (2025)
- [IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection](https://mlsanity.com/paper/2505.16258/) (2025)
- [Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.11852/) (2025)
- [SatireDecoder: Visual Cascaded Decoupling for Enhancing Satirical Image Comprehension](https://mlsanity.com/paper/2512.00582/) (2025)
- [Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2408.02595/) (2024)
- [Integrating Feedback Loss from Bi-modal Sarcasm Detector for Sarcastic Speech Synthesis](https://mlsanity.com/paper/2508.13028/) (2025)
- [Evaluating Multimodal Large Language Models on Spoken Sarcasm Understanding](https://mlsanity.com/paper/2509.15476/) (2025)
- [CFMS: Towards Explainable and Fine-Grained Chinese Multimodal Sarcasm Detection Benchmark](https://mlsanity.com/paper/2604.16372/) (2026)
- [A Comprehensive Review of Visual-Textual Sentiment Analysis from Social Media Networks](https://mlsanity.com/paper/2207.02160/) (2022)

## Abstract (arXiv)

Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (such as interjections and intensifiers), linguistic markers, and contextual information (such as user profiles, or past conversations) were used to detect the sarcastic tone. However, modern social media platforms allow to create multimodal messages where audiovisual content is integrated with the text, making the analysis of a mode in isolation partial. In our work, we first study the relationship between the textual and visual aspects in multimodal posts from three major social media platforms, i.e., Instagram, Tumblr and Twitter, and we run a crowdsourcing task to quantify the extent to which images are perceived as necessary by human annotators. Moreover, we propose two different computational frameworks to detect sarcasm that integrate the textual and visual modalities. The first approach exploits visual semantics trained on an external dataset, and concatenates the semantics features with state-of-the-art textual features. The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. Results show the positive effect of combining modalities for the detection of sarcasm across platforms and methods.