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Dataset for multimodal fake news detection and verification tasks
Alessandro Bondielli1, Pietro Dell'Oglio2, Alessandro Lenci3
1Department of Computer Science, University of Pisa, Largo Bruno Pontecorvo, 3, 56127, Pisa, Italy.
Data in Brief
|May 7, 2024
Summary
This study introduces a new Italian dataset for multimodal fake news detection, crucial for combating online disinformation. It aids in developing systems that analyze text and images to identify fake news effectively.
Area of Science:
- Natural Language Processing
- Computer Vision
- Information Science
Background:
- Online disinformation and fake news present a significant challenge, especially during breaking news events.
- While text is the primary medium, multimodal content (text, images) is increasingly used for spreading misinformation.
- Existing multimodal datasets are scarce, particularly for low-resource languages like Italian.
Discussion:
- This research releases a novel multimodal dataset for fake news detection in Italian.
- The dataset supports two sub-tasks: evaluating multimodal detection systems and analyzing text-image interplay in fake news.
- Crowdsourced labeling, enhanced with external knowledge, ensures data quality for classification tasks.
Key Insights:
- The dataset comprises social media posts and news articles, totaling 913 items for sub-task 1 and 1350 for sub-task 2.
- It facilitates research into how different modalities influence fake news interpretation.
- The availability of this dataset addresses a critical gap in low-resource multimodal fake news detection.
Outlook:
- Enables the development of more robust fake news detection models for the Italian language.
- Promotes further research into cross-modal analysis for misinformation detection.
- Contributes to building more resilient online information ecosystems.
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