While social bots can be used for various good causes, they can also be utilized to manipulate people and spread malware. Therefore, it is crucial to detect bots running on social media platforms. However, social bots are increasingly successful in creating human-like messages with the recent developments in artificial intelligence. Thus, we need more sophisticated solutions to detect them. In this study, we propose a novel deep learning architecture in which three long short-term memory (LSTM) models and a fully connected layer are utilized to capture complex social media activity of humans and bots. Since our architecture involves many components connected at different levels, we explore three learning schemes to train each component effectively. In our extensive experiments, we analyze the impact of each component of our architecture on classification accuracy using four different datasets. Furthermore, we show that our proposed architecture outperforms all baselines used in our experiments.

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Twitter 上基于深度学习的社交机器人检测

虽然社交机器人可用于各种公益事业，但它们也可用于操纵人和传播恶意软件。因此，检测在社交媒体平台上运行的机器人至关重要。然而，随着人工智能的最新发展，社交机器人在创建类似人类的消息方面越来越成功。因此，我们需要更复杂的解决方案来检测它们。在这项研究中，我们提出了一种新颖的深度学习架构，其中使用三个长短期记忆 (LSTM) 模型和一个完全连接的层来捕获人类和机器人的复杂社交媒体活动。由于我们的架构涉及许多在不同级别连接的组件，因此我们探索了三种学习方案来有效地训练每个组件。在我们广泛的实验中，我们使用四个不同的数据集分析了架构的每个组件对分类准确性的影响。此外，我们表明我们提出的架构优于我们实验中使用的所有基线。