Organizations: Wenxian Wang and Xingshu Chen are with the Key Laboratory of Data Protection and Intelligent Management, the Cyber Science Research Institute, Sichuan University, Chengdu 610207, China · Xiaohu Luo, Junfeng Hao and Haizhou Wang are with the School of Cyber Science and Engineering, Sichuan University, Chengdu 610207, China · Xiaoming Gu is with the State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China · Zhu Wang is with the Law School, Sichuan University, Chengdu 610207, China
Sarcasm is a rhetorical device that expresses criticism or emphasizes characteristics of certain individuals or situations through exaggeration, irony, or comparison. Existing methods for Chinese sarcasm detection are constrained by limited datasets and high construction costs, and they mainly focus on textual features, overlooking user-specific linguistic patterns that shape how opinions and emotions are expressed. This paper proposes a Generative Adversarial Network (GAN) and Large Language Model (LLM)-driven data augmentation framework to dynamically model users' linguistic patterns for enhanced Chinese sarcasm detection. First, we collect raw data from various topics on Sina Weibo. Then, we train a GAN on these data and apply a GPT-3.5 based data augmentation technique to synthesize an extended sarcastic comment dataset, named SinaSarc. This dataset contains target comments, contextual information, and user historical behavior. Finally, we extend the BERT architecture to incorporate multi-dimensional information, particularly user historical behavior, enabling the model to capture dynamic linguistic patterns and uncover implicit sarcastic cues in comments. Experimental results demonstrate the effectiveness of our proposed method. Specifically, our model achieves the highest F1-scores on both the non-sarcastic and sarcastic categories, with values of 0.9138 and 0.9151 respectively, which outperforms all existing state-of-the-art (SOTA) approaches. This study presents a novel framework for dynamically modeling users' long-term linguistic patterns in Chinese sarcasm detection, contributing to both dataset construction and methodological advancement in this field.
Figures & tables
Fig. 1: Here is an example demonstrating the importance of users’ long-term linguistic patterns for sarcasm detection. By learning such patterns from user historical behavior, our model can more accurately identify sarcastic comments.
Fig. 2: Modeling diagram of our proposed method.
Dataset
Source
Total
Sarcastic
Non-sarcastic
Method
Key Components
Tang et al. [ 39 ]
Plurk
950
950
0
Bootstrapping+Manual
Text
Lin et al. [ 40 ]
PTT
26,629
17,256
9,373
Lexicon-based
Text
Gong et al. [ 42 ]
Guanchazhe
91,782
2,486
89,296
Manual
Contextual Information
Xiang et al. [ 41 ]
Sina Weibo
8,702
968
7,734
Manual
Text
Zhang et al. [ 20 ]
Bilibili
79,045
2,814
76,231
Manual
Contextual Information
SinaSarc (Ours)
Sina Weibo
20,000
10,000
10,000
Data Augmentation
User Historical Behavior
TABLE I: Chinese Sarcasm Dataset Comparison
Category
Model
Features
Acc.
Non-sarcastic
Sarcastic
T
C
U
Pre.
Rec.
F1
Pre.
Rec.
F1
Machine Learning
Random Forest [ 51 ]
✓
0.8032
0.7985
0.8912
0.8423
0.8122
0.6766
0.7383
SVM [ 20 ]
✓
0.8183
0.8569
0.8307
0.8436
0.7668
0.8005
0.7833
AdaBoost [ 52 ]
✓
0.7231
0.7506
0.7946
0.7720
0.6775
0.6204
0.6477
Neural Network
FNN [ 53 ]
✓
0.8464
0.8610
0.8820
0.8714
0.8242
0.7953
0.8095
LSTM [ 28 ]
✓
✓
0.8428
0.8650
0.8692
0.8671
0.8106
0.8049
0.8078
TABLE II: Results of comparison of experimental
Fig. 3: Results of ablation experiments
Fig. 4: Results of noise experiment
Fig. 5: Results of robustness experiment
Fig. 6: Results of dataset size experiments
No.
Topic
Comment Hierarchy
Comments
Correct results
Test results
1
Lifestyle
Nested comment
你是真的懂设计 (You really know design)
Sarcastic
Non-sarcastic
2
Politics
Nested comment
成都省的GDP当然高了 (Chengdu province’s GDP is certainly high)
Sarcastic
Non-sarcastic
3
Entertainment
Top-level comment
这张票真值了，两个小时的电影感觉看了五个小时 (This movie ticket was totally worth it—two hours that felt like five!)
Sarcastic
Non-sarcastic
4
Relationships
Nested comment
男人到某一阶段就会自动解锁历史学家、哲学家、政治家等角色 (Men just naturally become historians, philosophers, and statesmen at a certain stage in life.)
Sarcastic
Non-sarcastic
5
Public Incidents
Top-level comment
这届网友可真“苛刻”，连救援人员的休息时间都要盯着，就怕他们累着 (This group of netizens is really “harsh” — they even keep an eye on the rescuers’ rest time, fearing they might get tired)
Non-sarcastic
Sarcastic
TABLE III: Examples of Sarcasm Detection Results
Fig. 7: Results of characterization learning experiment
Sarcasm detection, the identification of discrepancies between literal and intended meaning, is a fundamental task in affective computing. However, zero-shot instruction-tuned Large Language Models (LLMs) systematically over-predict the positive (sarcastic) class across the entire capability spectrum, while the prosodic cues humans rely on remain underexploited and transfer unevenly across languages. We introduce CHARM (Charge Calibration and Acoustic Rescue for Multimodal Sarcasm Detection), a training-free framework that couples two modules. Bidirectional Charge Calibration (BiCAL) steers the LLM toward opposing sarcastic and literal verdicts along a symmetric axis of charged prompts; the induced directional biases cancel by construction, and a simple aggregation recovers an unbiased pragmatic signal. Acoustic Late-Fusion Rescue (ALFR) then fuses the calibrated votes with prosodic descriptors and LLM-generated auditory-perception probes through a shallow classifier, actively down-weighting saturated text votes in favour of acoustic evidence. Without fine-tuning any backbone, BiCAL attains the highest reported zero-shot text-only Macro-F1 of 0.787 on MUStARD, while ALFR lifts weak backbones by up to +0.382 Macro-F1 on CMMA. A Stouffer meta-analysis confirms statistical significance on MUStARD and CMMA (Z = 13.89 and Z = 34.64, respectively; p < 10^-43). Our analysis further uncovers a cross-cultural prosodic decoupling: low-level acoustics fail to transfer across languages, whereas high-level perceptual abstractions remain robust. Together, these components yield an explainable, cross-lingual multimodal detector.
Qiyang Sun, Yi Chang, Yupei Li +3
GLAM – the Group on Language, Audio, & Music, Imperial College London, UK · College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, China · College of Computer Science and Electronic Engineering, Hunan University, China +2
Sarcasm detection remains challenging because sarcastic intent often depends on information across text, speech, and vision. We systematically evaluate large language models (LLMs) and multimodal LLMs (MLLMs) for sarcasm detection on English (MUStARD++) and Chinese (MCSD), considering both feature-extraction settings and direct classification. In the feature-extraction settings, audio provides the strongest unimodal performance on both datasets, while multimodal fusion shows dataset-dependent benefits. In direct classification, Qwen2.5-Omni achieves the strongest zero-shot performance among the evaluated models, with further substantial gains after fine-tuning. Error analysis further shows that additional modalities can both correct and introduce errors, highlighting the need for selective multimodal integration.
Multimodal sarcasm detection has recently garnered significant attention. However, existing benchmarks suffer from coarse-grained annotations and limited cultural coverage, which hinder research into fine-grained semantic understanding. To address this, we construct CFMS, the first fine-grained multimodal sarcasm dataset tailored for Chinese social media. It comprises 2,796 high-quality image-text pairs and provides a triple-level annotation framework: sarcasm identification, target recognition, and explanation generation. We find that the fine-grained explanation annotations effectively guide AI in generating images with explicit sarcastic intent. Furthermore, we curate a high-consistency parallel Chinese-English metaphor subset (200 entries each), revealing significant limitations of current models in metaphoric reasoning. To overcome the constraints of traditional retrieval methods, we propose a Reinforcement Learning-augmented In-Context Learning strategy (PGDS) to dynamically optimize exemplar selection. Extensive experiments demonstrate that CFMS provides a solid foundation for building reliable multimodal sarcasm understanding systems, and the PGDS method significantly outperforms existing baselines on key tasks. Our data and code are available at https://anonymous.4open.science/r/CFMS-E8F9.
Junzhao Zhang, Hsiu-Yuan Huang, Chenming Tang +2
National Key Laboratory for Multimedia Information Processing, Peking University · School of Computer Science, Peking University · School of Software and Microelectronics, Peking University