T$^\text{3}$SVFND: Towards an Evolving Fake News Detector for Emergencies with Test-time Training on Short Video Platforms

• URL: http://arxiv.org/abs/2507.20286v1
• Date: Sun, 27 Jul 2025 14:04:00 GMT
• Title: T$^\text{3}$SVFND: Towards an Evolving Fake News Detector for Emergencies with Test-time Training on Short Video Platforms
• Authors: Liyuan Zhang, Zeyun Cheng, Yan Yang, Yong Liu, Jinke Ma,
• Abstract summary: We propose a new fake news videos detection framework (T$3$SVFND) using Test-Time Training (TTT)<n>Specifically, we design a self-supervised auxiliary task based on Mask Language Modeling (MLM) that masks a certain percentage of words in text.<n>In the test-time training phase, the model adapts to the distribution of test data through auxiliary tasks.
• Score: 9.656794231914882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The existing methods for fake news videos detection may not be generalized, because there is a distribution shift between short video news of different events, and the performance of such techniques greatly drops if news records are coming from emergencies. We propose a new fake news videos detection framework (T$^3$SVFND) using Test-Time Training (TTT) to alleviate this limitation, enhancing the robustness of fake news videos detection. Specifically, we design a self-supervised auxiliary task based on Mask Language Modeling (MLM) that masks a certain percentage of words in text and predicts these masked words by combining contextual information from different modalities (audio and video). In the test-time training phase, the model adapts to the distribution of test data through auxiliary tasks. Extensive experiments on the public benchmark demonstrate the effectiveness of the proposed model, especially for the detection of emergency news.

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