结合主动学习与关系图卷积神经网络的社交机器人检测

社交机器人一直在应用中不断发展，并且为了逃避现有的检测方法，变得更加先进和复杂，较大地影响了原有部分社交机器人检测方法的效果.检测社交机器人成为了一项漫长而又艰巨的工作.在社交机器人检测领域中，目前存在着已公开相关数据集较少的情况，需要人工标注大量的数据.本文提出了一种结合主动学习与关系图卷积神经网络(RGCN)的检测方法——ALRGCN,用以解决人工标注大量数据成本较高的问题.其主要思路是利用主动学习方法来扩充标记数据集，以最大化人工标注的价值.主动学习利用种子选择算法构建初始训练集以及不确定性采样方法筛选出较高信息熵的样本，交由分类模型进行训练，旨在通过专业人员的经验来人工标注一些分类器难以分类的数据.鉴于社交机器人通常以集群的形式出现，本文引入了RGCN来捕捉其网络结构特征.RGCN可以有效地分析节点及其相邻节点的属性，进而帮助该节点进行分类.实验在TwiBot-20数据集上进行，通过对比进行使用的基线实验，ALRGCN在F1上取得了2.83%的提升.实验结果证明，ALRGCN在标注样本更小的情况下可以更有效地检测出社交机器人.

ocial bot detection based on active learning and relational graph convolutional neural networks

Social bots have been evolving over time, and they have become more advanced and sophisticated while avoiding existing detection methods. This has made some of the original social bot detection methods no longer superior and detecting social bots has become a long and arduous task. The field of social bot detection currently suffers from a small number of publicly available relevant datasets and requires manual annotation of a large amount of data. This paper propose ALRGCN, a detection framework that combines active learning with Relational Graph Convolutional Neural networks (RGCN), to address the problem of high cost of manually labeling large amounts of data. The main idea is to use active learning methods to expand the labeled dataset and maximize the value of manual labeling. Active learning uses a seed selection algorithm to construct an initial training set and an uncertainty sampling method to filter out samples with high information entropy for training by a classification model, aiming at classifying data that are prone to misclassification by a professional''s experience. Given that social bots usually appear as clusters, this paper introduces RGCN to capture its network structure features. RGCN can effectively analyze the attributes of a node and its neighboring nodes, which in turn helps that node to perform classification. The experiments are conducted on the TwiBot-20 dataset, and ALRGCN achieves a 2.83% improvement on F1 compared to the baseline experiments conducted for use. The experimental results demonstrate that ALRGCN can be more effective in detecting social bots with smaller labeled samples.