Multimodal Fake News Detection with Textual, Visual and Semantic Information

• Published in: Text, Speech, and Dialogue Vol. 12284; pp. 30 - 38
• Main Authors: Giachanou, Anastasia, Zhang, Guobiao, Rosso, Paolo
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Image-text similarity; Multimodal fake news detection; Textual features; Visual features
• ISBN: 9783030583224; 3030583228
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-58323-1_3

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.