  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.
