Abstract: 【Purpose/significance】Social media has not only changed the way news spreads and the way people get news, but also be?
come the main channel of fake news dissemination, which causes great harm to society.【Method/process】In order to effectively identi? fy the fake news published on social media, this article designs the text word embedding, text sentiment, image bottom layer, and im? age semantic feature representation methods to extract the image features in the tweet based on the analysis of the characteristics of the fake news content information and text feature information, a fake news detection model of multimodal feature fusion was construct? ed, and the performance of the model was verified using the MediaEval2015 dataset.【Result/conclusion】By comparing and analyzing the experimental results of different feature combinations, it is found that the multimodal model combining text features and image fea? tures can effectively improve the effect of fake news detection.【Innovation/limitation】A fake news detection model has been designed from the perspective of multimodality, which combines various features of text and image. However, using vector mosaic to realize fea? ture fusion can't achieve full complementarity of various features and easily cause dimension disaster.