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Model Bots, not Humans on Social Media

机译:模范机器人,而非社交媒体上的人类

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摘要

The Posting schedule reveals characteristic patterns of users on social media. Motivated by this knowledge, several researchers have modeled posting schedules and argued that deviation from the model indicates bot or spammer characteristics. It is true that circadian rhythms induce regularity in human posting behavior; however, in this paper, we show that this regularity is an individual trait and insufficient to develop a generic model. More surprisingly, we show that bots are more structured in their posting behaviors compared to humans by using a Convolutional Neural Network (CNN). More precisely, we demonstrate using Class Activation Maps that bots contain less entropy than humans. Thus, we conclude that bots are more amenable to generic models than humans. We evaluate the hypothesis on more than 32 million posts from 12 thousand Twitter users with 97% accuracy.
机译:发布时间表揭示了社交媒体上用户的特征模式。受此知识的激励,几位研究人员对发布时间表进行了建模,并认为与模型的偏差表明了僵尸程序或垃圾邮件发送者的特征。昼夜节律的确会引起人类张贴行为的规律性。但是,在本文中,我们证明了这种规律性是个人特征,不足以开发通用模型。更令人惊讶的是,通过使用卷积神经网络(CNN),我们证明了与人相比,机器人的发帖行为更加结构化。更准确地说,我们证明了使用类激活图,机器人包含的熵少于人类。因此,我们得出的结论是,机器人比人类更适合通用模型。我们对来自12,000个Twitter用户的3200万条帖子的假设进行了评估,准确性为97%。

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