The proliferation of generative models has presented significant challenges in distinguishing authentic human-authored content from deepfake content. Collaborative human efforts, augmented by AI tools, present a promising solution. In this study, we explore the potential of DeepFakeDeLiBot, a deliberation-enhancing chatbot, to support groups in detecting deepfake text. Our findings reveal that group-based problem-solving significantly improves the accuracy of identifying machine-generated paragraphs compared to individual efforts. While engagement with DeepFakeDeLiBot does not yield substantial performance gains overall, it enhances group dynamics by fostering greater participant engagement, consensus building, and the frequency and diversity of reasoning-based utterances. Additionally, participants with higher perceived effectiveness of group collaboration exhibited performance benefits from DeepFakeDeLiBot. These findings underscore the potential of deliberative chatbots in fostering interactive and productive group dynamics while ensuring accuracy in collaborative deepfake text detection. \textit{Dataset and source code used in this study will be made publicly available upon acceptance of the manuscript.

翻译：生成模型的普及给区分真实人类创作内容与深度伪造内容带来了显著挑战。借助人工智能工具增强的人类协作努力，为这一难题提供了有前景的解决方案。在本研究中，我们探讨了增强审议的聊天机器人DeepFakeDeLiBot在支持小组检测深度伪造文本方面的潜力。研究发现，与个人努力相比，基于小组的问题解决方式能显著提高识别机器生成段落的准确性。虽然与DeepFakeDeLiBot的互动并未整体上带来显著的性能提升，但它通过促进参与者更深入的参与、共识的建立以及基于推理的话语频率和多样性，改善了小组动态。此外，感知群体协作有效性较高的参与者，从DeepFakeDeLiBot中获得了性能收益。这些发现凸显了审议型聊天机器人在促进互动且高效的小组动态、同时确保深度伪造文本协作检测准确性方面的潜力。\textit{本研究所使用的数据集和源代码将在论文被接收后公开提供。}