Spammer Detection on Short Video Applications: A New Challenge and Baselines

Users can interact with the advertisements and share their impressions through the review system on short video applications. However, spammers may post false or malicious comments to mislead normal users due to profit-driven reasons, damaging the community's positive atmosphere. In this paper, we introduce a new challenge of spammer detection on short video applications, where the multi-modal information of videos and reviews plays a more critical role than the spam relation graph. Then we propose SPAM-3, a novel baseline to detect spam reviews with multi-modal representation using attentive heterogeneous graph convolution. Our approach balances multi-modal representation fusion and graph relation extraction, enabling fine-grained interaction and generating discriminative features for the spammer classification task. Experiments show that SPAM-3 outperforms all the baseline models. We further demonstrate our method via experimental analysis and ablations.