# AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection

arXiv: https://arxiv.org/abs/2410.15591 · HTML: https://mlsanity.com/paper/2410.15591/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 21 Oct 2024.

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

- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [PTR: Prompt Tuning with Rules for Text Classification](https://mlsanity.com/paper/2105.11259/) (2021)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection](https://mlsanity.com/paper/2309.12247/) (2023)
- [Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models](https://mlsanity.com/paper/2310.04027/) (2023)
- [Towards Few-Shot Fact-Checking via Perplexity](https://mlsanity.com/paper/2103.09535/) (2021)
- [SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2109.08306/) (2021)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection](https://mlsanity.com/paper/2309.16424/) (2023)
- [Affect Recognition in Conversations Using Large Language Models](https://mlsanity.com/paper/2309.12881/) (2023)
- [Team QUST at SemEval-2023 Task 3: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting Online News Genre, Framing and Persuasion Techniques](https://mlsanity.com/paper/2304.04190/) (2023)
- [Fake News Detection: Experiments and Approaches beyond Linguistic Features](https://mlsanity.com/paper/2109.12914/) (2021)
- [Similarity-Aware Multimodal Prompt Learning for Fake News Detection](https://mlsanity.com/paper/2304.04187/) (2023)
- [Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text](https://mlsanity.com/paper/2402.11934/) (2024)
- [Instruction Tuning Vs. In-Context Learning: Revisiting Large Language Models in Few-Shot Computational Social Science](https://mlsanity.com/paper/2409.14673/) (2024)
- [Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection](https://mlsanity.com/paper/2407.12880/) (2024)
- [Large Visual-Language Models Are Also Good Classifiers: A Study of In-Context Multimodal Fake News Detection](https://mlsanity.com/paper/2407.12879/) (2024)

## Built on by (most cited first, 2 of 2)

- [The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection](https://mlsanity.com/paper/2601.15316/) (2026)
- [UNITE-FND: Reframing Multimodal Fake News Detection through Unimodal Scene Translation](https://mlsanity.com/paper/2502.11132/) (2025)

## Abstract (arXiv)

Detecting fake news in large datasets is challenging due to its diversity and complexity, with traditional approaches often focusing on textual features while underutilizing semantic and emotional elements. Current methods also rely heavily on large annotated datasets, limiting their effectiveness in more nuanced analysis. To address these challenges, this paper introduces Emotion-Aware Multimodal Fusion Prompt LEarning (AMPLE) framework to address the above issue by combining text sentiment analysis with multimodal data and hybrid prompt templates. This framework extracts emotional elements from texts by leveraging sentiment analysis tools. It then employs Multi-Head Cross-Attention (MCA) mechanisms and similarity-aware fusion methods to integrate multimodal data. The proposed AMPLE framework demonstrates strong performance on two public datasets in both few-shot and data-rich settings, with results indicating the potential of emotional aspects in fake news detection. Furthermore, the study explores the impact of integrating large language models with this method for text sentiment extraction, revealing substantial room for further improvement. The code can be found at :urlhttps://github.com/xxm1215/MMM2025few-shot/