Enhancing GNN-based CQA Spam Detection: Question-Answer-Pair Perspective with Supervised Neighbor Selection

Community question answering (CQA) portals have become very popular platforms attracting numerous participants to share and acquire knowledge and information on the Internet. However, many malicious users post suggestive questions and deceptive answers to promote a target (product or service), which can extremely distort the users' decision, and make the CQA environment less credible. Although various methods have been proposed to detect the fraud content from CQA, most of them detect deceptive questions and answers separately, thus suffer the problem of interactive information missing. In this paper, we take the Question-Answer-Pair (QAP) as the detecting target and propose a GNN-based method to detect the spam activities in the CQA. Specifically, we also design a label-aware neighbor selection module based on supervised learning to select more appropriate neighbors for attributes aggregating in G NN. Extensive experiments are conducted in a real-world dataset and results demonstrate the advantage and effectiveness of the proposed model for CQA spam detection.