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Multimodal Fake News Detection with Textual,Visual and Semantic Information - 2020

Multimodal Fake News Detection With Textual,Visual And Semantic Information

Research Area:  Machine Learning


Recent years have seen a rapid growth in the number of fake news that are posted online. Fake news detection is very challenging since they are usually created to contain a mixture of false and real information and images that have been manipulated that confuses the readers. In this paper, we propose a multimodal system with the aim to differentiate between fake and real posts. Our system is based on a neural network and combines textual, visual and semantic information. The textual information is extracted from the content of the post, the visual one from the image that is associated with the post and the semantic refers to the similarity between the image and the text of the post. We conduct our experiments on three standard real world collections and we show the importance of those features on detecting fake news.


Author(s) Name:  Anastasia Giachanou, Guobiao Zhang & Paolo Rosso

Journal name:  

Conferrence name:  International Conference on Text, Speech, and Dialogue

Publisher name:  Springer

DOI:  10.1007/978-3-030-58323-1_3

Volume Information: