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FakeSV: A Multimodal Benchmark with Rich Social Context for Fake News Detection on Short Video Platforms

AAAI 2023 · Video › Video-language understanding · approach: Event and anomaly reasoning in video · 103 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SV-FEND's multimodal content and social-context fusion instead of the strongest reported single-modality BERT baselineFakeSV · FakeSV event-level five-fold CVaccuracy: 76.82$_{\pm3.33}$ -> 79.31$_{\pm2.75}$Helped
add news content (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 74.89 -> 79.31Helped
add text (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 75.37 -> 79.31Helped
add title (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 77.28 -> 79.31Helped
add transcript (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 77.89 -> 79.31Helped
add visual features (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 77.97 -> 79.31Helped
add keyframes (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 78.49 -> 79.31Helped
add video clips (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 78.95 -> 79.31Helped
add audio (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 78.95 -> 79.31Helped
add social context (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 78.62 -> 79.31Helped
add user features (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 78.76 -> 79.31Helped
add comments (ablation)SV-FEND · FakeSV event-level five-fold CVaccuracy: 79.09 -> 79.31Helped

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