Abstract
The escalation of false information related to the massive use of social media has became a challenging problem and great is the effort of the research community in providing effective solutions to detecting it. Fake news are spreading since decades, but with the rise of social media the nature of misinformation has evolved from text based modality to visual modalities, such as images, audio and video. Therefore, the identification of media-rich fake news requires an approach that exploits and effectively combines the information acquired from different multimodal categories. Multimodality is a key approach to improve fake news detection, but effective solutions supporting it are still poorly explored. More specifically, many different works exist that investigate if a text, an image or a video is fake or not, but effective research on a real multimodal setting, ‘fusing’ the different modalities with their different structure and dimension is still an open problem. The paper is a focused survey concerning a very specific topic that is the use of Deep Learning methods (DL) for multimodal fake news detection on social media.
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This work was partially supported by project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU.
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Caroprese, L., Comito, C., Zumpano, E. (2023). Fake News on Social Media: Current Research and Future Directions. In: Comito, C., Talia, D. (eds) Pervasive Knowledge and Collective Intelligence on Web and Social Media. PerSOM 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 494. Springer, Cham. https://doi.org/10.1007/978-3-031-31469-8_4
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