AI 中文总结
本文综述ChatGPT时代社交机器人检测的挑战与机遇，指出当前技术空白，提出生成式数据、多模态跨平台检测、低资源语言扩展及联邦学习协作等未来研究方向。
AI 中文摘要
我们全面概述了在基于人工智能的复杂聊天机器人兴起的背景下，社交机器人检测所面临的挑战与机遇。通过审视社交机器人检测技术的当前最新水平以及迄今为止更为突出的实际应用，我们识别出该领域的空白与新兴趋势，重点关注应对人工智能生成的对话和行为所引发的独特挑战。我们提出了社交机器人检测中潜在的有前景的机遇和研究方向，包括：（i）使用生成式智能体进行合成数据生成、测试和评估；（ii）基于协调与影响的网络和行为特征，需要多模态和跨平台检测；（iii）将机器人检测扩展到非英语和低资源语言环境的机遇；以及（iv）开发协作式联邦学习检测模型的空间，这些模型有助于促进不同组织和平台之间的合作，同时保护用户隐私。
英文摘要
We present a comprehensive overview of the challenges and opportunities in social bot detection in the context of the rise of sophisticated AI-based chatbots. By examining the state of the art in social bot detection techniques and the more salient real-world application to date, we identify gaps and emerging trends in the field, with a focus on addressing the unique challenges posed by AI-generated conversations and behaviors. We suggest potentially promising opportunities and research directions in social bot detection, including (i) the use of generative agents for synthetic data generation, testing and evaluation; (ii) the need for multimodal and cross-platform detection based on network and behavioral signatures of coordination and influence; (iii) the opportunity to extend bot detection to non-English and low-resource language settings; and, (iv) the room for development of collaborative, federated learning detection models that can help facilitate cooperation between different organizations and platforms while preserving user privacy.
Journal refFirst Monday, 28(6), 2023
