cgao-comp/C3N

The code of the paper "Improving Multimodal Fake News Detection by Leveraging Cross-modal Content Correlation"

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Experimental

This project helps social media analysts and content moderators identify fake news by analyzing both the text and images within posts. It takes social media posts (like tweets or Weibo updates) containing both text and images, processes them, and then determines whether the content is likely to be false. The ideal user is someone responsible for content integrity or information verification on social media platforms.

Use this if you need to automatically detect fake news by cross-referencing information from both text and associated images in social media content.

Not ideal if you only work with text-based content or if you need to verify factual claims outside of a social media context.

fake-news-detection social-media-moderation content-verification information-integrity multimodal-analysis
No License No Package No Dependents
Maintenance 10 / 25
Adoption 6 / 25
Maturity 8 / 25
Community 0 / 25

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16

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Language

Python

License

Last pushed

Feb 22, 2026

Commits (30d)

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