Summary of Ample: Emotion-aware Multimodal Fusion Prompt Learning For Fake News Detection, by Xiaoman Xu et al.
AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection
by Xiaoman Xu, Xiangrun Li, Taihang Wang, Ye Jiang
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The AMPLE framework, a multimodal fusion prompt learning approach, is introduced to detect fake news by combining text sentiment analysis with multimodal data and hybrid prompt templates. The framework extracts emotional elements from texts using sentiment analysis tools and integrates multimodal data through Multi-Head Cross-Attention (MCA) mechanisms and similarity-aware fusion methods. AMPLE demonstrates strong performance on two public datasets in both few-shot and data-rich settings, highlighting the potential of emotional aspects in fake news detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fake news detection is a challenging task due to the diversity and complexity of large datasets. Current methods often focus on textual features while underutilizing semantic and emotional elements. The AMPLE framework aims to address this by combining text sentiment analysis with multimodal data and hybrid prompt templates. By leveraging sentiment analysis tools, AMPLE extracts emotional elements from texts and integrates multimodal data using Multi-Head Cross-Attention (MCA) mechanisms and similarity-aware fusion methods. This approach demonstrates strong performance on two public datasets in both few-shot and data-rich settings. |
Keywords
» Artificial intelligence » Cross attention » Few shot » Prompt
