Abstract
Social bots play a significant role in many online social networks (OSN) as they imitate human behavior. This fact raises difficult questions about their capabilities and potential risks. Given the recent advances in Generative AI (GenAI), social bots are capable of producing highly realistic and complex content that mimics human creativity. As the malicious social bots emerge to deceive people with their unrealistic content, identifying them and distinguishing the content they produce has become an actual challenge for numerous social platforms. Several approaches to this problem have already been proposed in the literature, but the proposed solutions have not been widely evaluated. To address this issue, we evaluate the behavior of a text-based bot detector in a competitive environment where some scenarios are proposed: \textit{First}, the tug-of-war between a bot and a bot detector is examined. It is interesting to analyze which party is more likely to prevail and which circumstances influence these expectations. In this regard, we model the problem as a synthetic adversarial game in which a conversational bot and a bot detector are engaged in strategic online interactions. \textit{Second}, the bot detection model is evaluated under attack examples generated by a social bot; to this end, we poison the dataset with attack examples and evaluate the model performance under this condition. \textit{Finally}, to investigate the impact of the dataset, a cross-domain analysis is performed. Through our comprehensive evaluation of different categories of social bots using two benchmark datasets, we were able to demonstrate some achivement that could be utilized in future works.
Abstract (translated)
社交机器人在很多在线社交网络（OSN）中扮演着重要的角色，因为它们模仿人类行为。这一事实引发了关于其能力和潜在风险的困难问题。考虑到最近的生成人工智能（GenAI）进步，社交机器人能够产生高度逼真和复杂的內容，模仿人类的创造力。随着恶意社交机器人通过不现实的內容欺骗人们的出现，识别它们并区分它们产生的内容已成为许多社交平台的实际挑战。 在文献中已经提出了几种解决这个问题的方法，但所提出的解决方案尚未得到广泛评估。为了解决这个问题，我们在一个竞争的环境中评估了一个基于文本的机器人检测器的行为：\textit{首先}，我们研究了机器人之间的拉锯战。分析哪个 party 更有可能获胜以及哪些情况会影响这些期望很有趣。在这方面，我们将问题建模为一个合成对抗游戏，其中聊天机器人和机器人检测器进行 strategic online interactions。\textit{其次}，我们分析了由社交机器人生成的攻击样本来评估机器人检测器的表现。因此，我们用攻击样本来污染数据集，并在此条件下评估了模型性能。\textit{最后}，为了研究数据集的影响，进行跨领域分析。通过使用两个基准数据集全面评估不同种类的社交机器人，我们能够证明未来工作中可以利用的一些成就。
URL
https://arxiv.org/abs/2405.02016
