融合情绪感知与多模态数据，提升假新闻检测效果
AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection
- 引入情感分析与多模态交叉注意力融合文本情绪特征
- 在少样本和全量数据下均显著优于现有方法
- 适合关注情感与多模态融合的虚假信息研究者
由于假新闻的多样性与复杂性，大规模数据下的检测极具挑战，传统方法多聚焦于文本特征，忽视语义与情感元素。当前方法又严重依赖大量标注数据，限制了对细微语境的分析能力。为此，本文提出情感感知多模态融合提示学习框架（AMPLE），通过情感分析工具提取文本情绪要素，并利用多头交叉注意力（MCA）与相似性感知融合机制整合多模态数据。该框架在两个公开数据集上均展现出优异性能，无论在少样本还是数据丰富的设置下，均实现显著提升，验证了情感因素在假新闻检测中的关键作用。此外，研究探索了大语言模型在文本情感提取中的应用，揭示了进一步优化空间。代码已开源。
原文摘要 · Abstract (English)
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-\textbf{A}ware \textbf{M}ultimodal Fusion \textbf{P}rompt \textbf{L}\textbf{E}arning (\textbf{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 :\url{https://github.com/xxm1215/MMM2025_few-shot/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。