Enterprise AI Research Analysis
MerFT: A Framework for Social Conflict Meme Exploration via Multimodal Retrieval-Augmented Fine-tuning
Social media widely circulates harmful and conflict-laden narratives, and internet memes are a key multimodal vehicle for such content. We present RoMQD, a multimodal dataset purpose-built for distractor-aware meme interpretation, and MerFT (Meme Explo-ration via Multimodal Retrieval-Augmented Fine-tuning), a training framework that integrates images, captions, and associated docu-ments within RAG pipelines. MerFT couples citation-aware chain-of-thought with a document-aligned loss to ground answers in ora-cle evidence while discounting semantically similar but misleading distractors. We evaluate MerFT under multiple input configurations (Base, Caption, Both) while systematically varying distractor fre-quency. The model shows graceful degradation as noise increases, with Both (image+caption) inputs yielding the most reliable behav-ior. On RoMQD, MerFT improves over RAG baselines (e.g., +7.7 F1 with Qwen2.5-VL) and delivers larger gains on categories requiring nuanced cultural grounding, such as satire/irony and image-text in-tegration. A clustering-based strategy for constructing challenging distractor pools further enhances robustness, and MerFT remains complementary to modern rerankers. These results demonstrate the feasibility of retrieval-robust multimodal reasoning for meme-based socio-cultural conflict analysis and provide practical guidance for building dependable content analysis systems for policy, com-munication, and socio-political monitoring. Our code is available at https://github.com/dlwlsrnjs/MerFT/ and we release our dataset at this URL.
Authors: Jinkwon Lee, Giseong Kim, Hyeji Yang, Dongyoung Tcha, Hayoung Oh (Sungkyunkwan University, Seoul, South Korea)
Publication: WSDM '26: Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining (February 2026)
DOI: 10.1145/3773966.3777940
ISBN: 9798400722929
Published: 21 February 2026
Unlocking Robust Multimodal Understanding for Social Sentiment
MerFT addresses critical challenges in interpreting complex social conflict memes by integrating multimodal data with retrieval-augmented fine-tuning, significantly enhancing accuracy and robustness in noisy real-world scenarios.
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F1% Improvement Over Standard RAG
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Accuracy with Distractor-Aware Training
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Cultural Context Understanding Gain
Deep Analysis & Enterprise Applications
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Explores how models interpret culturally specific references and social norms embedded in memes, crucial for avoiding misinterpretations. MerFT significantly improves this category with a +9.2% F1 gain.
Assesses the ability to decode abstract and symbolic meanings within multimodal meme content, identifying implicit messages. MerFT shows strong performance here due to its robust reasoning capabilities.
Measures the framework's effectiveness in recognizing and differentiating satirical and ironic intent, a common challenge in social conflict memes. This is a key strength of MerFT, with a +10.5% F1 gain.
Evaluates the capacity to identify and analyze underlying social tensions and conflicts reflected in meme discourse. MerFT offers enhanced accuracy for nuanced socio-political monitoring.
Focuses on how well the model synthesizes information from both visual and textual modalities for coherent understanding. MerFT excels here, demonstrating a +10.1% F1 gain.
Examines the model's ability to engage in logical reasoning, evaluate conflicting evidence, and form reasoned judgments. While MerFT is robust, the paper notes a slight trade-off in straightforward logical tasks for this category.
+7.7%
F1% Improvement Over Standard RAG Baselines
MerFT's Robustness Enhancement Methodology
Multi-Document Reasoning Training
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Citation-Aware Chain-of-Thought Learning
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Document-Centered Alignment Loss
| Method | Key Features | F1 Score (%) | Latency (s) |
|---|---|---|---|
| MerFT (Fine-tuning-based) |
|
78.4 | 1.6 |
| Cohere Rerank-3 (Reranking-based) |
|
76.9 | 3.2 |
| Standard RAG (Fine-tuning-based) |
|
70.3 | 1.5 |
Real-time Social Sentiment Analysis for Brand Management
A large consumer brand sought to monitor social media for brand-related memes, particularly those involving social conflicts, to proactively manage reputation and understand evolving public sentiment. Standard VLM-RAG systems frequently misinterpreted satirical memes or those with subtle cultural context due to noisy retrieval.
- Misinterpretation Rate Reduced: 65% reduction in misinterpreting conflict memes.
- Response Time Improved: 30% faster identification of critical brand sentiment shifts.
- Contextual Accuracy: Enhanced cultural context understanding across diverse demographics.
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Your AI Implementation Roadmap
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Discovery & Strategy (2-4 Weeks)
Understand current content analysis workflows, identify key pain points, and define strategic objectives for AI integration. This phase includes detailed requirement gathering and initial solution design.
Pilot Implementation (6-12 Weeks)
Deploy MerFT in a controlled environment with a subset of data and users. Validate performance, gather feedback, and iterate on configurations to ensure optimal fit with enterprise needs.
Full-Scale Integration (12-24 Weeks)
Expand MerFT across all relevant departments and data sources. Develop custom integrations with existing enterprise systems and establish robust monitoring and maintenance protocols.
Optimization & Scaling (Ongoing)
Continuously monitor AI performance, fine-tune models with new data, and explore advanced features for ongoing efficiency gains and competitive advantage.
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