In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally successful in their malicious intent due to the existence of OSN users who follow them and thereby increase their reputation in the network. Analysis of the socialbot networks and their users is vital to comprehend the socialbot problem from target users’ perspective. In this paper, we present a machine learning-based approach for characterizing and detecting socialbot targets, i.e., users who are susceptible to be trapped by the socialbots. We model OSN users based on their identity and behavior information, representing the static and dynamic components of their personality. The proposed approach classifies socialbot targets into three categories viz. active, reactive, and inactive users. We evaluate the proposed approach using three classifiers over a dataset collected from a live socialbot injection experiment conducted on Twitter. We also present a comparative evaluation of the proposed approach with a state-of-the-art method and show that it performs significantly better. On feature ablation analysis, we found that network structure and user intention and personality related dynamic features are most discriminative, whereas static features show the least impact on the classification. Additionally, following rate, multimedia ratio, and follower rate are most relevant to segregate different categories of the socialbot targets. We also perform a detailed topical and behavioral analysis of socialbot targets and found active users to be suspicious. Further, joy and agreeableness are the most dominating personality traits among the three categories of the users.

中文翻译：

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一种用于Twitter上的Socialbot目标检测的机器学习方法

在在线社交网络（OSN）中，社交机器人负责各种恶意活动，并且主要通过编程来模仿人类行为，以绕过现有的检测系统。由于存在跟随他们的OSN用户，因此社交机器人的恶意意图通常是成功的，从而提高了他们在网络中的声誉。对社交机器人网络及其用户的分析对于从目标用户的角度理解社交机器人问题至关重要。在本文中，我们提出了一种基于机器学习的方法来表征和检测社交机器人目标，即易受社交机器人陷阱的用户。我们根据OSN用户的身份和行为信息对他们进行建模，这些信息代表其个性的静态和动态组成部分。所提出的方法将社交机器人目标分为三类。活动，反应和非活动用户。我们使用三个分类器对从Twitter上进行的实时socialbot注入实验收集的数据集评估提出的方法。我们还介绍了使用最新方法对提议的方法进行的比较评估，并表明它的性能明显更好。在特征消融分析中，我们发现网络结构以及与用户意图和个性相关的动态特征具有最大的判别力，而静态特征对分类的影响最小。此外，追踪率，多媒体比率和追踪者率与分离社交机器人目标的不同类别最相关。我们还对socialbot目标进行了详细的主题和行为分析，发现活动用户可疑。此外，在三类用户中，快乐和愉快是最主要的人格特质。