Thousands of videos are posted online every day. The affordability of video editing tools and social networks has facilitated the creation and spread of videos carrying disinformation, i.e. fake videos. Previous attempts to categorize disinformation have focused on content analysis and ascertaining the intention of creators. To extend these approaches, it is beneficial to incorporate the perspective of other fields that study the trustworthiness of records, such as archival science, to help detect and categorize fake videos. This paper proposes to leverage archival science in combination with computer engineering to devise a new framework for detecting and categorizing fake videos. In doing so, the paper offers a case study of the way in which Computational Archival Science, which blends archival and computational thinking, can be used to contribute to a novel approach towards solving the problem of fake videos.
Extending the Scope of Computational Archival Science: A Case Study on Leveraging Archival and Engineering Approaches to Develop a Framework to Detect and Prevent “Fake Video”
Monday, February 24, 2020