PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910547090341888 |
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| author | Paudel, Pujan Ling, Chen Blackburn, Jeremy Stringhini, Gianluca |
| author_facet | Paudel, Pujan Ling, Chen Blackburn, Jeremy Stringhini, Gianluca |
| contents | Images are a powerful and immediate vehicle to carry misleading or outright false messages, yet identifying image-based misinformation at scale poses unique challenges. In this paper, we present PIXELMOD, a system that leverages perceptual hashes, vector databases, and optical character recognition (OCR) to efficiently identify images that are candidates to receive soft moderation labels on Twitter. We show that PIXELMOD outperforms existing image similarity approaches when applied to soft moderation, with negligible performance overhead. We then test PIXELMOD on a dataset of tweets surrounding the 2020 US Presidential Election, and find that it is able to identify visually misleading images that are candidates for soft moderation with 0.99% false detection and 2.06% false negatives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_20987 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter Paudel, Pujan Ling, Chen Blackburn, Jeremy Stringhini, Gianluca Computer Vision and Pattern Recognition Computers and Society Images are a powerful and immediate vehicle to carry misleading or outright false messages, yet identifying image-based misinformation at scale poses unique challenges. In this paper, we present PIXELMOD, a system that leverages perceptual hashes, vector databases, and optical character recognition (OCR) to efficiently identify images that are candidates to receive soft moderation labels on Twitter. We show that PIXELMOD outperforms existing image similarity approaches when applied to soft moderation, with negligible performance overhead. We then test PIXELMOD on a dataset of tweets surrounding the 2020 US Presidential Election, and find that it is able to identify visually misleading images that are candidates for soft moderation with 0.99% false detection and 2.06% false negatives. |
| title | PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter |
| topic | Computer Vision and Pattern Recognition Computers and Society |
| url | https://arxiv.org/abs/2407.20987 |