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

TL;DR

This paper introduces a novel two-branch graph neural network that effectively fuses multimodal image and text data to improve the automatic detection of marketing intentions online.

Contribution

It proposes a two-branch collaborative GCN framework specifically designed for multimodal data fusion in marketing intention detection, addressing existing modality gap challenges.

Findings

Achieves superior performance in marketing intention detection tasks.

Effectively fuses multimodal data through collaborative graph convolution.

Demonstrates robustness across different datasets.

Abstract

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.