# RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection Using In-Context Learning Based on Emotional Information

arXiv: https://arxiv.org/abs/2406.11093 · HTML: https://mlsanity.com/paper/2406.11093/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Privacy and security of RAG, one of 15 directions in Retrieval-augmented generation.
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 16 Jun 2024.

## Placed in

- Retrieval-augmented generation / [Privacy and security of RAG](https://mlsanity.com/rag/privacy-and-security-of-rag/)

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning](https://mlsanity.com/paper/2301.11916/) (2023)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [MDFEND: Multi-domain Fake News Detection](https://mlsanity.com/paper/2201.00987/) (2022)
- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [A Practical Survey on Zero-shot Prompt Design for In-context Learning](https://mlsanity.com/paper/2309.13205/) (2023)
- [Let's Learn Step by Step: Enhancing In-Context Learning Ability with Curriculum Learning](https://mlsanity.com/paper/2402.10738/) (2024)
- [FactLLaMA: Optimizing Instruction-Following Language Models with External Knowledge for Automated Fact-Checking](https://mlsanity.com/paper/2309.00240/) (2023)
- [Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation](https://mlsanity.com/paper/2403.14952/) (2024)
- [SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics](https://mlsanity.com/paper/2005.04114/) (2020)
- [LEMMA: Towards LVLM-Enhanced Multimodal Misinformation Detection with External Knowledge Augmentation](https://mlsanity.com/paper/2402.11943/) (2024)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Domain Re-Modulation for Few-Shot Generative Domain Adaptation](https://mlsanity.com/paper/2302.02550/) (2023)
- [Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning](https://mlsanity.com/paper/2311.11551/) (2023)
- [Re-Search for The Truth: Multi-round Retrieval-augmented Large Language Models are Strong Fake News Detectors](https://mlsanity.com/paper/2403.09747/) (2024)
- [Misconfidence-based Demonstration Selection for LLM In-Context Learning](https://mlsanity.com/paper/2401.06301/) (2024)
- [A Deep Learning Approach for Automatic Detection of Fake News](https://mlsanity.com/paper/2005.04938/) (2020)

## Built on by (most cited first, 14 of 14)

- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [A Multi-Agent Framework with Automated Decision Rule Optimization for Cross-Domain Misinformation Detection](https://mlsanity.com/paper/2503.23329/) (2025)
- [VaxGuard: A Multi-Generator, Multi-Type, and Multi-Role Dataset for Detecting LLM-Generated Vaccine Misinformation](https://mlsanity.com/paper/2503.09103/) (2025)
- [CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit](https://mlsanity.com/paper/2410.12061/) (2024)
- [The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence](https://mlsanity.com/paper/2601.05478/) (2026)
- [Rumor Detection by Multi-task Suffix Learning based on Time-series Dual Sentiments](https://mlsanity.com/paper/2502.14383/) (2025)
- [SCOUT-RAG: Scalable and Cost-Efficient Unifying Traversal for Agentic Graph-RAG over Distributed Domains](https://mlsanity.com/paper/2602.08400/) (2026)
- [ConspEmoLLM-v2: A robust and stable model to detect sentiment-transformed conspiracy theories](https://mlsanity.com/paper/2505.14917/) (2025)
- [Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection](https://mlsanity.com/paper/2605.19285/) (2026)
- [MisSpans: Fine-Grained False Span Identification in Cross-Domain Fake News](https://mlsanity.com/paper/2601.04857/) (2026)
- [Cross-Domain Fake News Detection on Unseen Domains via LLM-Based Domain-Aware User Modeling](https://mlsanity.com/paper/2602.01726/) (2026)
- [Culturally Adaptive Explainable LLM Assessment for Multilingual Information Disorder: A Human-in-the-Loop Approach](https://mlsanity.com/paper/2603.27356/) (2026)
- [Latent Causal Void: Explicit Missing-Context Reconstruction for Misinformation Detection](https://mlsanity.com/paper/2605.12156/) (2026)
- [ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies](https://mlsanity.com/paper/2605.27045/) (2026)

## Abstract (arXiv)

Misinformation is prevalent in various fields such as education, politics, health, etc., causing significant harm to society. However, current methods for cross-domain misinformation detection rely on effort- and resource-intensive fine-tuning and complex model structures. With the outstanding performance of LLMs, many studies have employed them for misinformation detection. Unfortunately, they focus on in-domain tasks and do not incorporate significant sentiment and emotion features (which we jointly call em affect). In this paper, we propose RAEmoLLM, the first retrieval augmented (RAG) LLMs framework to address cross-domain misinformation detection using in-context learning based on affective information. RAEmoLLM includes three modules. (1) In the index construction module, we apply an emotional LLM to obtain affective embeddings from all domains to construct a retrieval database. (2) The retrieval module uses the database to recommend top K examples (text-label pairs) from source domain data for target domain contents. (3) These examples are adopted as few-shot demonstrations for the inference module to process the target domain content. The RAEmoLLM can effectively enhance the general performance of LLMs in cross-domain misinformation detection tasks through affect-based retrieval, without fine-tuning. We evaluate our framework on three misinformation benchmarks. Results show that RAEmoLLM achieves significant improvements compared to the other few-shot methods on three datasets, with the highest increases of 15.64%, 31.18%, and 15.73% respectively. This project is available at https://github.com/lzw108/RAEmoLLM.