FakingRecipe: Detecting Fake News on Short Video Platforms from the Perspective of Creative Process
This addresses the problem of fake news spreading on short video platforms, which is a growing threat to online information ecosystems, and is incremental by introducing a novel perspective on creation rather than content analysis.
The paper tackled fake news detection on short video platforms by analyzing the creative process behind video production, resulting in the FakingRecipe model that outperforms existing methods on both a new English dataset (FakeTT) and an existing Chinese dataset (FakeSV).
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 short videos. It captures the fake news preferences in material selection from sentimental and semantic aspects and considers the traits of material editing from spatial and temporal aspects. To improve evaluation comprehensiveness, we first construct FakeTT, an English dataset for this task, and conduct experiments on both FakeTT and the existing Chinese FakeSV dataset. The results show FakingRecipe's superiority in detecting fake news on short video platforms.