将用户行为视为视频，用图像分类方法识别网络垃圾账号。
User to Video: A Model for Spammer Detection Inspired by Video Classification Technology
- 把用户行为看作视频帧，用像素化和图像生成技术建模。
- 在微博和推特数据集上，准确率优于现有最先进方法。
- 适合对反垃圾、行为模式分析感兴趣的从业者或研究者。
本文受视频分类技术启发，提出一种基于用户视频化的垃圾账号检测模型（UVSD）。将用户行为子空间视为帧图像，连续帧构成用户行为视频。首先提出用户像素化算法（user2piexl），将用户立场量化为像素的RGB值；其次设计行为到图像转换算法（behavior2image），通过表示学习对用户关系进行低秩密集向量化，并结合剪裁与扩散算法完成帧图像构建；最后利用时间特征构造用户行为视频，并结合视频分类算法识别垃圾账号。在公开数据集WEIBO和TWITTER上的实验表明，该模型性能优于当前最先进的方法。
原文摘要 · Abstract (English)
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.
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