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Inspired by the fact that spreading and collecting information through theInternet becomes the norm, more and more people choose to post for-profitcontents (images and texts) in social networks. Due to the difficulty ofnetwork censors, malicious marketing may be capable of harming society.Therefore, it is meaningful to detect marketing intentions onlineautomatically. However, gaps between multimodal data make it difficult to fuseimages and texts for content marketing detection. To this end, this paperproposes Two-Branch Collaborative Graph Neural Networks to collaborativelyrepresent multimodal data by Graph Convolution Networks (GCNs) in an end-to-endfashion. Experimental results demonstrate that our proposed method achievessuperior graph classification performance for marketing intention detection.

Towards Better Graph Representation: Two-Branch Collaborative Graph Neural Networks for Multimodal Marketing Intention Detection