Spammer Detection on Short Video Applications: A new Challenge and Baselines
Muyang Yi, Dong Liang, Rui Wang, Yue Ding, Hongtao Lu
Abstract
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. We demonstrate SPAM-3 via experiments and analysis.
BibTeX
@inproceedings{icassp2023_spammerdetection,
title = {Spammer Detection on Short Video Applications: A new Challenge and Baselines},
author = {Muyang Yi and Dong Liang and Rui Wang and Yue Ding and Hongtao Lu},
booktitle = {ICASSP 2023},
year = {2023}
}