G-HFIN: Graph-based Hierarchical Feature Integration Network for propaganda detection of We-media news articles

Abstract

In the era of We-media, articles are written by independently individuals that are not officially registered with the authorities. Propaganda hidden in the We-media articles have the potential to polarize public opinion and influence the mindset of the target audience. Currently, graph neural networks (GNNs) have been remarkably successful in Natural Language Processing (NLP). However, there are still some challenges to apply existing GNN-based for propaganda detection due to the limitation of extracting diverse word dependencies and capturing non-consecutive and long-range context. In this paper, we have proposed a Graph-based Hierarchical Feature Integration Network (abbreviated as G-HFIN) for Propaganda Detection. Specifically, semantic, syntactic, and sequential features are extracted to construct three heterogeneous graphs. Then, the Residual-connected Dual-layer Coarsening and Refining procedures (abbreviated as RDCR) are proposed to promote information interactions between distant nodes that are not directly connected, preserving both local and global node information during the intra-graph information propagation. Subsequently, an Attention-based Three-channel Feature Integration (abbreviated as ATFI) is proposed to harmonize sequence, semantic and syntactic information from three channels during the inter-graph information enhancement. Intra-graph and Inter-graph Joint Information Propagation is to implement homogeneous and heterogeneous information interaction respectively. Finally, these news representations are pooled and fed into the propaganda detection classifier. The experiments on three public datasets demonstrate that our model has outperformed state-of-the-art methods.