High-Quality Content Recognition in Social Media

How to automatically recognize high-quality content from a large number of multimedia articles is one of the core functions of information recommendation,search engine and other systems.Existing methodsrely on a large amount of manual annotated data in training.In addition,visual and social information in social media is often not considered.This paper proposes a high-quality article content recognition model of graph convolutional network based on positive and unlabeled learning,named GCN-PU,which uses a heterogeneous network to simultaneously model the text and social information of social media articles in a unified framework.A graph convolutional network is used on the network to fuse the information to obtain high-order features.In addition,the global visual layout information of the multimedia article is used to capture the comprehensive visual quality characteristics of the article,which is used to complement the high-order features of the graph convolutional network output.Finally,we introduce positive and unlabeled learning into the training and loss functions to take advantage of the large amount of unlabeled article information in social media.Experimental results on real social media datasets show that GCN-PU has improved F-score by more than 3%over current best approaches.