基于双层采样主动学习的社交网络虚假用户检测方法

社交网络的飞速发展给用户带来了便捷，但是社交网络开放性的特点使得其容易受到虚假用户的影响.虚假用户借用社交网络传播虚假信息达到自身的目的，这种行为严重影响着社交网络的安全性和稳定性.目前社交网络虚假用户的检测方法主要通过用户的行为、文本和网络关系等特征对用户进行分类，由于人工标注用户数据需要的代价较大，导致分类器能够使用的标签样本不足.为解决此问题，本文提出一种基于双层采样主动学习的社交网络虚假用户检测方法，该方法使用样本不确定性、代表性和多样性3个指标评估未标记样本的价值，并使用排序和聚类相结合的双层采样算法对未标记样本进行筛选，选出最有价值的样本给专家标注，用于对分类模型的训练.在Twitter、Apontador和Youtube数据集上的实验说明本文所提方法在标签样本数量不足的情况下，只使用少量有标签样本就可以达到与有监督学习接近的检测效果；并且，对比其他主动学习方法，本文方法具有更高的准确率和召回率，需要的标签样本数量更少.

Two-layer Sampling Active Learning Algorithm for Social Spammer Detection

With the rapid development of social network, more and more people join in social network to make friends and share their views. However, social network is always suffering from fake accounts due to its openness. Fake accounts, also called spammers, always spread spam information to achieve their own purpose, which have destroyed the security and reliability of social network. Existing detection methods extract behaviour, text and relationship features of users, and then use machine learning algorithms to identify social spammers. But machine learning algorithms often suffer from insufficiently labeled training data. Aiming to solve this problem, we propose an efficient algorithm, called two-layer sampling active learning, to construct an accurate classifier with minimum labeled samples. We present three criteria (uncertainty, representative and diversity) to quantity the value of unlabeled samples, using the combination of sorting and clustering to actively select samples with max uncertainty, max representative and max diversity. Experimental results on Twitter, Apontador, and Youtube datasets prove the efficiency of our approach, and better precision and recall of our approach than other active learning methods.