TL;DR
This paper introduces FakingRecipe, a novel model that detects fake news in short videos by analyzing the creative process behind their production, leveraging multimodal features and new datasets.
Contribution
It proposes a creative process-aware approach for fake news detection in short videos, incorporating insights from material selection and editing traits, and introduces the FakeTT dataset.
Findings
FakingRecipe outperforms existing methods in fake news detection.
The model effectively captures semantic and sentimental cues in video materials.
Experimental results demonstrate the model's robustness across datasets.
Abstract
As short-form video-sharing platforms become a significant channel for news consumption, fake news in short videos has emerged as a serious threat in the online information ecosystem, making developing detection methods for this new scenario an urgent need. Compared with that in text and image formats, fake news on short video platforms contains rich but heterogeneous information in various modalities, posing a challenge to effective feature utilization. Unlike existing works mostly focusing on analyzing what is presented, we introduce a novel perspective that considers how it might be created. Through the lens of the creative process behind news video production, our empirical analysis uncovers the unique characteristics of fake news videos in material selection and editing. Based on the obtained insights, we design FakingRecipe, a creative process-aware model for detecting fake news…
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