ArXiv Preprint
With information consumption via online video streaming becoming increasingly
popular, misinformation video poses a new threat to the health of the online
information ecosystem. Though previous studies have made much progress in
detecting misinformation in text and image formats, video-based misinformation
brings new and unique challenges to automatic detection systems: 1) high
information heterogeneity brought by various modalities, 2) blurred distinction
between misleading video manipulation and ubiquitous artistic video editing,
and 3) new patterns of misinformation propagation due to the dominant role of
recommendation systems on online video platforms. To facilitate research on
this challenging task, we conduct this survey to present advances in
misinformation video detection research. We first analyze and characterize the
misinformation video from three levels including signals, semantics, and
intents. Based on the characterization, we systematically review existing works
for detection from features of various modalities to techniques for clue
integration. We also introduce existing resources including representative
datasets and widely used tools. Besides summarizing existing studies, we
discuss related areas and outline open issues and future directions to
encourage and guide more research on misinformation video detection. Our
corresponding public repository is available at
https://github.com/ICTMCG/Awesome-Misinfo-Video-Detection.
Yuyan Bu, Qiang Sheng, Juan Cao, Peng Qi, Danding Wang, Jintao Li
2023-02-07
