Abstract
Disinformation has become an increasingly significant problem in today’s digital world, spreading rapidly across various multimedia platforms. To combat this issue, we propose a novel hybrid intelligence framework that combines the power of deep learning and fuzzy logic-based methods to detect multimodal disinformation content. The framework comprises two main components: the multimodal feature analyzer and the multimodal disinformation content detector. In the multimodal feature measurement step, we extract features from different modalities of a multimedia piece and then use deep learning methods to obtain a set of different measures. Finally, in the multimodal disinformation content detection step, we use a fuzzy logic-based method to detect disinformation content based on previously obtained multimodal features. To validate the effectiveness of our proposed framework, we conducted experiments using a dataset of TikTok videos containing various forms of disinformation. Our experiments demonstrated the viability of our approach and its potential to be applied to other social media platforms.
The Spanish Government has partially supported this work under the grant SAFER: PID2019-104735RB-C42 (ERA/ERDF, EU).
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Guerrero-Sosa, J.D.T., Romero, F.P., Montoro-Montarroso, A., Menendez, V.H., Serrano-Guerrero, J., Olivas, J.A. (2023). A Fuzzy Approach to Detecting Suspected Disinformation in Videos. In: Larsen, H.L., Martin-Bautista, M.J., Ruiz, M.D., Andreasen, T., Bordogna, G., De Tré, G. (eds) Flexible Query Answering Systems. FQAS 2023. Lecture Notes in Computer Science(), vol 14113. Springer, Cham. https://doi.org/10.1007/978-3-031-42935-4_12
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