  Inspired by the fact that spreading and collecting information through the
Internet becomes the norm, more and more people choose to post for-profit
contents (images and texts) in social networks. Due to the difficulty of
network censors, malicious marketing may be capable of harming society.
Therefore, it is meaningful to detect marketing intentions online
automatically. However, gaps between multimodal data make it difficult to fuse
images and texts for content marketing detection. To this end, this paper
proposes Two-Branch Collaborative Graph Neural Networks to collaboratively
represent multimodal data by Graph Convolution Networks (GCNs) in an end-to-end
fashion. Experimental results demonstrate that our proposed method achieves
superior graph classification performance for marketing intention detection.
