KEYWORDS: Education and training, Data modeling, Multimedia, Machine learning, Deep learning, Web 2.0 technologies, Semantics, Internet, Feature extraction, Video
With the explosive growth of amount of information exchanged over the Internet, we have witnessed fast propagation of mis/disinformation. Such trend of mis/disinformation must be detected early and curbed effectively in order to mitigate its potential harm to the nation and society. Our previous work successfully identified distinctive patterns of the propagation of true and fake news in the form of text over social media, with Twitter as a case study. In this work, our goal is to extend the target to include multimedia mis/disinformation and study the characteristics of their dissemination using machine learning based techniques. We also aim to investigate countermeasures that can be employed to slow down or prevent further propagation based on the identified characteristics.
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