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Deep Learning Based Social Bot Detection on Twitter
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DOI：10.1109/TIFS.2023.3254429.png)
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摘要
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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.
Keyword:
Social bot detection
online account classification
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5.3K
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A novel framework for detecting social bots with deep neural networks and active learning一种基于深度神经网络和主动学习的社交机器人检测框架
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