Abstract
This article is inspired by video classification technology. If the user behavior subspace is viewed as a frame image, consecutive frame images are viewed as a video. Following this novel idea, a model for spammer detection based on user videoization, called UVSD, is proposed. Firstly, a user2piexl algorithm for user pixelization is proposed. Considering the adversarial behavior of user stances, the user is viewed as a pixel, and the stance is quantified as the pixel's RGB. Secondly, a behavior2image algorithm is proposed for transforming user behavior subspace into frame images. Low-rank dense vectorization of subspace user relations is performed using representation learning, while cutting and diffusion algorithms are introduced to complete the frame imageization. Finally, user behavior videos are constructed based on temporal features. Subsequently, a video classification algorithm is combined to identify the spammers. Experiments using publicly available datasets, i.e., WEIBO and TWITTER, show an advantage of the UVSD model over state-of-the-art methods.
Abstract (translated)
本文受到视频分类技术的启发。如果将用户行为子空间视为一个帧图像，那么连续的帧图像则被视为一段视频。基于这一新颖的理念，提出了名为UVSD（User Videoization-based Spammer Detection）的检测垃圾信息发送者的模型。 首先，提出了一种用于用户像素化的user2pixel算法。考虑到用户的对抗性立场行为，将每个用户视为一个像素，并且使用RGB值量化其立场。 其次，为了将用户行为子空间转换为帧图像，提出了behavior2image算法。通过表示学习执行了低秩稠密向量化来处理子空间中的用户关系，在此过程中引入切割和扩散算法以完成帧图的创建过程。 最后，根据时间特征构建用户行为视频，并结合视频分类算法识别垃圾信息发送者。 在使用公开数据集（如WEIBO和TWITTER）进行实验时，UVSD模型表现出优于现有方法的优势。
URL
https://arxiv.org/abs/2510.06233
