Recently, due to the rapid growth of online social networks (OSNs) such as Facebook, Twitter, Weibo, etc. the number of machine accounts/social bots that mimic human users has increased. Along with the development of artificial intelligence (AI), social bots are designed to become smarter and more sophisticated in their efforts at replicating the normal behaviors of human accounts. Constructing reliable and effective bot detection mechanisms is this considered crucial to keep OSNs clean and safe for users. Despite the rapid development of social bot detection platforms, recent state-of-the-art systems still encounter challenges which are related to the model’s generalization (and whether it can be adaptable for multiple types of OSNs) as well as the great efforts needed for feature engineering. In this paper, we propose a novel approach of applying network representation learning (NRL) to bot/spammer detection, called Bot2Vec. Our proposed Bot2Vec model is designed to automatically preserve both local neighborhood relations and the intra-community structure of user nodes while learning the representation of given OSNs, without using any extra features based on the user’s profile. By applying the intra-community random walk strategy, Bot2Vec promises to achieve better user node embedding outputs than recent state-of-the-art network embedding baselines for bot detection tasks. Extensive experiments on two different types of real-word social networks (Twitter and Tagged) demonstrate the effectiveness of our proposed model. The source code for implementing the Bot2Vec model is available at: https://github.com/phamtheanhphu/bot2vec

近来，由于快速增长ø n第小号ocial Ñ etworks（的OSN），例如如Facebook，Twitter，微博等计算机帐户/社交机器人模拟人类用户已经增加的数量。随着发展的一个rtificial我情报（AI），社交机器人旨在在复制人类帐户的正常行为时变得更加聪明和精巧。构建可靠且有效的漫游器检测机制被认为对于保持OSN清洁和用户安全至关重要。尽管社交机器人检测平台发展迅速，但最新的先进系统仍然面临与模型的泛化（以及模型是否可适应多种OSN）相关的挑战，并且需要付出巨大的努力。特征工程。在本文中，我们提出了一种将网络表示学习（NRL）应用于Bot /垃圾邮件发送者检测的新颖方法，称为Bot2Vec。我们提出的Bot2Vec模型旨在在学习给定OSN表示的同时自动保留本地邻居关系和用户节点的社区内部结构，而无需使用基于用户个人资料的任何其他功能。通过应用社区内随机游走策略，Bot2Vec有望实现比最近用于机器人检测任务的最新网络嵌入基线更好的用户节点嵌入输出。在两种不同类型的实词社交网络（Twitter和Tagged）上的大量实验证明了我们提出的模型的有效性。可以在以下位置获得用于实现Bot2Vec模型的源代码：https：//github.com/phamtheanhphu/bot2vec 通过应用社区内随机游走策略，Bot2Vec有望实现比最近用于机器人检测任务的最新网络嵌入基线更好的用户节点嵌入输出。在两种不同类型的实词社交网络（Twitter和Tagged）上的大量实验证明了我们提出的模型的有效性。可以在以下位置获得用于实现Bot2Vec模型的源代码：https：//github.com/phamtheanhphu/bot2vec 通过应用社区内随机游走策略，Bot2Vec有望实现比最近用于机器人检测任务的最新网络嵌入基线更好的用户节点嵌入输出。在两种不同类型的实词社交网络（Twitter和Tagged）上的大量实验证明了我们提出的模型的有效性。可以在以下位置获得用于实现Bot2Vec模型的源代码：https：//github.com/phamtheanhphu/bot2vec